PhD opportunities

Modern engineering systems often need reliable predictions from limited, low-quality information. A demanding example is tracking satellites and space debris in Low Earth Orbit (LEO): this requires making accurate predictions despite many sources of uncertainty, including imperfect initial conditions, limited fidelity of atmospheric models, unknown satellite attitudes and physical properties, and unmodelled manoeuvres. These effects quickly cause predicted positions to drift so that tracking can be lost altogether.

In this project you will develop novel algorithms for autonomous sensing and tracking under uncertainty, in the form of tasking algorithms and data reduction pipelines for the portable CICLOPS telescope at Imperial.

The project involves theoretical development of sensing and state estimation algorithms as well as hands-on data acquisition. In a first task, you will design astrometric and orbit determination methods to compute object trajectories from noisy image sequences and to characterise the associated measurement uncertainty.

In the second task, you will develop sensor tasking and state estimation algorithms that use these measurements to keep track of a catalogue of satellite trajectories through long gaps due to limited availability of observations. This must be achieved with only partial knowledge of satellite characteristics and behaviour.

During the project, you will plan observation campaigns and conduct observations of LEO satellites from Imperial campuses using CICLOPS. The observation campaigns will demonstrate closing the loop between sensor tasking, data reduction, and state estimation.

What you will do:

• Build autonomous astrometric and orbit determination pipelines for the CICLOPS telescope specific to LEO objects.
• Plan and run LEO observation campaigns, process and manage the output image datasets.
• Develop new algorithms for nonlinear state estimation under sparse measurements and low signal-to-noise ratios.
• Benchmark nonlinear state estimation methods against state-of-the-art.
• Disseminate your work through articles and presentations in high-impact journals and conferences.
• Collaborate with researchers within Imperial and the Centre for Protection of Dark and Quiet Skies of the International Astronomical Union.
• Develop documented, reproducible code.

Benefits of the studentship:

• Deep technical expertise in:
  • state estimation and inference for nonlinear dynamical systems,
  • orbit determination and autonomous sensor tasking,
  • telescope observations and advanced astrometry.
• Advanced scientific programming skills.
• Ability to plan and deliver an independent, complex R&D project.
• Oral and written scientific communication skills.
• Experience in research collaboration and technical leadership within a convergence science project.

Supervisory team:

• Dr Davide Amato, Associate Professor in Computational Astrodynamics, Department of Aeronautics
• Dr Dave Clements, Associate Professor of Astrophysics, Department of Physics
• Dr Michael Peel, Research Associate, Department of Physics

For further information: please email Dr Davide Amato (d.amato@imperial.ac.uk) and Dr Dave Clements (d.clements@imperial.ac.uk).

Modern society depends on precise and reliable time. Telecommunications, transport, energy networks, financial systems, and emerging autonomous technologies all rely on accurate synchronisation. At present, much of this timing is provided by satellite-based systems such as GNSS. While highly capable, these systems are vulnerable to disruption through jamming, spoofing, obstruction, or loss of access, particularly in dense urban environments, indoors, underground, or in contested settings. There is therefore a growing need for resilient, terrestrial timing solutions that can operate independently of satellites.

This PhD project focuses on the use of deployable optical atomic clocks as local or regional time references for terrestrial infrastructure. It builds on an experimental programme at Imperial centred on high-performance optical clocks based on neutral strontium. These systems have reached a level of maturity where the central challenge is no longer record laboratory performance, but adapting advanced clock technology for reliable operation outside the laboratory.

The project sits at the interface between atomic physics and engineering and is jointly supervised across the Department of Physics and the Department of Civil and Environmental Engineering. During the PhD, the student will work hands-on with optical atomic clock systems, studying clock operation, stability, calibration, and sensitivity to environmental effects under realistic conditions. In parallel, the research will address system-level questions such as timing distribution, monitoring, resilience, and practical integration with terrestrial infrastructure relevant to transport systems and digital connectivity.

A central aim of the project is to design, implement, and validate a prototype terrestrial timing node capable of maintaining reliable local time when satellite timing is degraded or unavailable. The work will remove key barriers to miniaturisation by replacing metre-scale optical reference cavities with atomic references, enabling field-ready clocks that operate for weeks with picosecond-level timing drift, sufficient to maintain critical services during disruptions.

While the primary focus is on real-world deployment and societal impact, the project is embedded in an environment that also supports research in fundamental physics, including precision measurement and quantum technologies. The student will receive interdisciplinary training spanning experimental physics and infrastructure engineering, develop strong experimental and analytical skills, and gain experience working at the boundary between fundamental science and operational systems. The project provides preparation for careers in academia, industry, national laboratories, and the growing quantum and infrastructure sectors, and forms part of the cohort-based School of Convergence Science doctoral programme.

International students: May apply to this project.

Supervisors and contacts

• Professor Buchmueller,  Professor of Physics, Department of Physics; o.buchmueller@imperial.ac.uk 
• Professor Washington Yotto Ochieng, Head of Department, Department of Civil and Environmental Engineering; w.ochieng@imperial.ac.uk 
• Professor Mohammed Quddus, Chair in Intelligent Transport Systems, Department of Civil and Environmental Engineering;  m.quddus@imperial.ac.uk 
• Dr Joseph Cotter, Associate Professor in Quantum Innovation
  Department of Physics; j.cotter@imperial.ac.uk  

This PhD supports the design of sustainable space vehicles by improving our understanding of the environmental impacts of space access. The expected rapid growth of the space sector could cause substantial harm, from air pollution to changes in extreme weather. However the environmental impacts of a satellite launch remain relatively unexplored. This inhibits engineers from designing sustainable solutions and increases the likelihood of poorly targeted regulation.

These challenges are compounded by the widespread use of conventional climate metrics in determining policy. These metrics traditionally link radiative forcing to changes in global mean surface temperature. However, a more nuanced approach is needed when considering the environmental impacts of space launch activity, seeking to understand the implications of stratospheric change even without surface warming.

In this PhD, you will take the first step towards establishing the new field of Sustainable Space Engineering by answering two key questions across three sub-projects (SPs):

1. What are the unrecognized environmental impacts of launch and re-entry?
2. How can space vehicle design or operations be modified to minimise said impacts?

In SP1, you will use the state-of-the-art Whole Atmosphere Community Climate Model to perform simulations of current-generation launch vehicles for comparison with near-future launch scenarios. This will include potential new launch vehicles in addition to representation of re-entering satellites from mega-constellations. Simulation output will be used to diagnose the atmosphere’s chemical and dynamical response to different design choices and operational parameters.

In SP2, you will develop new metrics of environmental impact tailored to high-altitude emissions. This will interrogate how changes in stratospheric radiative forcing modify both stratospheric dynamics and tropospheric conditions in novel ways. This will allow you to determine the effects of (e.g.) conventional “net zero” radiative forcing due to a mix of shortwave cooling and longwave heating – e.g. from a stratospheric aerosol debris layer.

Lastly, in SP3 you will synthesize these outcomes into new environmental impact metrics to assess the sustainability of potential near-future launch vehicles. These findings will inform the development of sustainable design strategies and regulation in space engineering.

This PhD is a unique opportunity to work with experts in sustainable engineering and climate physics. You will become proficient in atmospheric simulation and knowledgeable in space sector engineering. You can expect to become a leader in the emerging field of sustainable space engineering, developing the necessary insight into the interplay between Earth science, space sector engineering, and policy to make access to space more sustainable.

Supervisory team

Dr Sebastian Eastham, Associate Professor in Sustainable Aviation, Department of Aeronautics
Professor Helen Brindley, Professor in Earth Observation, Department of Physics
Dr Collen Golja, Postdoctoral Fellow, Department of Physics

Contacts

Supervisors: Sebastian D Eastham (s.eastham@imperial.ac.uk), Colleen Golja (c.golja@imperial.ac.uk), Helen E Brindley (h.brindley@imperial.ac.uk

In 1994, Peter Shor introduced a polynomial-time quantum algorithm capable of breaking the public-key cryptographic schemes that underpin today’s digital infrastructure. In response, the cryptographic community has turned to post-quantum cryptography (PQC): the development of classical cryptographic protocols believed to remain secure against quantum attacks. These protocols are typically based on mathematical problems that are conjectured to require super-polynomial time to solve, even for quantum computers.

A central mathematical foundation of many PQC schemes is lattice-based cryptography, in particular the Shortest Vector Problem (SVP), which asks for the shortest non-zero vector in a lattice. Structured lattices—most notably module lattices—have been introduced to improve efficiency. The NIST PQC process has attracted worldwide attention, culminating in July 2022 with the selection of CRYSTALS-KYBER as the key encapsulation mechanism, and CRYSTALS-Dilithium and FALCON as digital signature schemes, which rely fundamentally on structured lattices.

Although lattice problems are widely believed to remain hard in the post-quantum era, the security foundations of lattice-based cryptography—especially for structured lattices—are far less understood than those of conventional cryptography. This PhD project aims to address this gap by developing quantum cryptanalytic techniques to study the resilience of lattice-based PQC schemes. By combining tools from algebraic number theory, lattice algorithms, and quantum computing, the project will provide a deeper understanding of the mathematical and computational security of structured lattices, both classically and quantumly.

Benefit to the student 

The PhD student will benefit from working in an interdisciplinary team jointly supervised by Professor Cong Ling in PQC and Professor Florian Mintert in quantum mechanics and quantum computing.

Contact details

• Professor Cong Ling, Professor in Information Theory and Cryptography
  Department of Electrical and Electronic Engineering (c.ling@imperial.ac.uk)
• Professor Florian Mintert, Professor of Physics, Department of Physics (f.mintert@imperial.ac.uk) 

Organophosphorus compounds are widely studied in chemistry, toxicology, and analytical science. In certain exposure scenarios, including accidental or malicious release of toxic organophosphorus substances, the human body forms stable phosphonate biomarkers that can be detected in blood and related biological samples. Reliable measurement of these biomarkers is critical for exposure assessment, medical decision-making, and forensic investigation.

Currently, liquid chromatography–mass spectrometry (LC-MS) is the gold standard for phosphonate analysis. While highly sensitive and selective, LC-MS is slow, resource-intensive, and poorly suited to rapid or large-scale screening due to complex sample preparation and long analysis times.

This PhD project will demonstrate that ambient mass spectrometry can provide a faster, high-throughput alternative. The work will focus on laser-ablation rapid evaporative ionisation mass spectrometry (LA-REIMS), which enables direct analysis with minimal preparation and acquisition times of tenths of a second per sample. By removing chromatographic separation, LA-REIMS has the potential to greatly simplify analytical workflows while retaining useful chemical specificity for screening applications.

A particular emphasis will be placed on dried blood spots, a low-cost and minimally invasive sampling format well suited to high-throughput analysis, transport, and storage. Subject to progress, methods may also be extended to selected environmental samples such as soil or water.

The project builds on preliminary work between Imperial College London and the Norwegian Defence Research Establishment (FFI), which demonstrated the feasibility of phosphonate biomarker detection using LA-REIMS at relevant sensitivity levels.

What the student will do:

• Develop LA-REIMS methods for phosphonate biomarker detection
• Benchmark ambient MS performance against LC-MS reference methods
• Lead peer-reviewed publications and international conference presentations
• Work with FFI to support real-world translation of the methodology

Training will be provided in advanced mass spectrometry, laser ablation, analytical chemistry, and data analysis, within an interdisciplinary environment spanning physics, chemistry, engineering, and resilience.

Benefits to the student:

• Hands-on experience with state-of-the-art mass spectrometers and laser systems
• Strong interdisciplinary training in analytical chemistry and applied physical science
• Embedded international collaboration and conference travel
• Excellent preparation for careers in academia, analytical laboratories, instrumentation companies, and applied research organisations

Contacts and Supervision

Lead Supervisor
Dr Robbie Murray | Department of Physics, Imperial College London robert.murray10@imperial.ac.uk

Co-Supervisors
Prof Leon Barron | School of Public Health, Imperial College London leon.barron@imperial.ac.uk

Prof Zoltán Takáts | Department of Metabolism, Digestion and Reproduction, Imperial College London
z.takats@imperial.ac.uk

Dr Ingvild Comfort Hvinden | Norwegian Defence Research Establishment (FFI), Instituttveien 20, Kjeller, Norway

Prof Peter Biggins | Centre for Active Resilience and Security, Department of Civil Engineering, Imperial College London

This PhD project will develop a new class of non-line-of-sight (NLoS) free-space optical communication systems based on ultraviolet-C (UV-C) laser pulses generated by nonlinear optical techniques, together with high-speed two-dimensional (2D) semiconductor detectors. Unlike conventional optical links, which rely on direct line-of-sight propagation, UV-C light is strongly scattered by the atmosphere. This strong multiple scattering redistributes optical energy over a wide range of angles, allowing information-carrying light to reach a receiver even when the transmitter and receiver are not directly visible to one another. This property enables optical communication “round corners” in complex or obstructed environments where infrared optical or RF systems perform poorly.

Exploiting recent breakthroughs in nonlinear UV-C pulse generation and room-temperature UV-C detection¹, the project will translate emerging photonic and materials capabilities into a laboratoryscale communication demonstrator. UV-C wavelengths (100–280 nm) offer several advantages for free-space communication, including low solar background and the scattering-dominated propagation that underpins robust NLoS links. However, practical implementation has so far been limited by the lack of compact, efficient nonlinear UV-C sources, suitable high-bandwidth detectors, and a system-level understanding of scattering-dominated channels. This project directly addresses these challenges through an integrated programme spanning nonlinear photonics, materials-enabled detection, and communication system design.

A key element of the PhD will be the development and optimisation of compact nonlinear UV-C laser sources, tailoring pulse energy, repetition rate, stability, and beam characteristics for efficient information transfer. While the primary application will be NLoS communication, the student will also have scope to explore novel UV-C source development more broadly, with potential relevance to areas such as spectroscopy or precision materials processing. This work will build on the host laboratory’s expertise in ultrafast lasers, nonlinear optics and frequency conversion.

In parallel, the student will integrate 2D-material UV-C photodetectors into high-speed receiver architectures, characterising temporal response, noise, and dynamic range under realistic link conditions. Limited exposure to 2D-material growth and fabrication at the University of Nottingham will provide additional insight into detector materials and performance.

A central component of the project is the study of NLoS UV-C channel physics. The student will quantify scattering-mediated propagation of UV-C pulses, develop physical and statistical channel models, and identify modulation and coding strategies consistent with the combined constraints of source, detector, and channel. These elements will be brought together in a laboratory-scale NLoS communication demonstrator, enabling evaluation of achievable data rates, robustness, and energy efficiency.

This is an inherently multidisciplinary PhD project spanning physics and electrical engineering. The student will develop a theoretical understanding of nonlinear optics, optoelectronic device physics, and communication theory, while gaining extensive practical experience in ultrafast optics, electronics, and systems engineering. They will graduate with a rare cross-disciplinary skill set at the interface of physics and engineering, supported by experience in state-of-the-art facilities and external collaboration. This training will position them well for careers in high-technology industry, 
particularly photonics, communications R&D, and security, or for progression to postdoctoral research in leading international laboratories

¹ B. T. Dewes et al., Light: Science & Applications 14, 384 (2025). https://doi.org/10.1038/s41377-025-02042-2

Contacts and supervisory team

• Prof J. Tisch (Lead supervisor, Department of Physics) john.tisch@imperial.ac.uk  
• Prof A. S. Holmes (Department of Electrical and Electronic Engineering) a.holmes@imperial.ac.uk 

Across Sub-Saharan Africa, millions of smallholder farmers are increasingly exposed to dangerous heat. Agriculture in the region relies heavily on manual labour, and rising temperatures are increasing the risk of heat-related illness, injury, and reduced ability to work. These effects extend beyond physical health, threatening livelihoods, wellbeing, food security, and national economies. Heat-related productivity losses in agriculture are estimated to have already reduced Africa’s GDP by around 4%.

This PhD project addresses heat-related health and productivity challenges by combining two promising approaches: agroforestry and sensor technology. Agroforestry—the integration of trees into farming systems—is a widely promoted climate adaptation and mitigation strategy, with about 7% of Africa’s land committed under climate policies. Trees can reduce local temperatures by up to 6°C, yet their direct benefits for farmers’ health and productivity remain poorly understood. At the same time, wearable health devices and environmental sensors are increasingly used to monitor heat stress in athletes and workers, but their application in smallholder farming, particularly in low-income settings, is still limited.

The project aims to develop and test a low-cost system that integrates health, productivity, and environmental monitoring to assess whether agroforestry and real-time heat alerts can help farmers work more safely in hot conditions while maintaining productivity. The student will complete three components. First, they will develop a practical monitoring and alert framework based on a reviewing relevant literature alongside input from experts across relevant disciplines. Second, they will adapt and pilot the framework in a real-world African setting, comparing farmers working in agroforestry and non-agroforestry systems. They will collect data on physiological responses to heat (such as heart rate, body temperature, hydration, and energy use), productivity (including movement patterns, time spent working, and work output), and environmental conditions. Third, the student will use participatory engagement and co-design with farmers, other end users, and experts to identify needs, constraints, and opportunities for future development of affordable, practical, scalable, and context-appropriate sensor-based heat adaptation tools and smart agroforestry practices.

The study will take place at an established agroforestry research site in Kenya or Zambia. The student will be supported by a multidisciplinary supervisory team with expertise in heat physiology, health and environmental sensors, environmental epidemiology, farming practices and work behaviour, agroforestry and its microclimate, alongside strong regional and local partnerships through organisations promoting agroforestry practice and resilient agriculture locally and across the African region.

What the student will do and benefits of the studentship

This project builds on the disciplines of engineering, climate change, environmental and occupational health, and international development.

The PhD student will design and test a low-cost monitoring and alert system to investigate how heat affects farmers’ health and productivity, and how agroforestry can reduce these risks. They will work with wearable health sensors, environmental monitoring tools, and field-based measures of labour productivity, carrying out interdisciplinary research at the interface of environmental science, health, and agriculture. The student will also work closely with farmers and practitioners through participatory research and co-design activities.

Through this studentship, the student will develop a strong foundation in climate change and health, agroforestry and sensor-based approaches to climate adaptation. They will gain specialist expertise in assessing heat stress impacts on health and labour productivity, and in applying wearable and environmental sensors in real-world settings. The project will provide hands-on experience of field research in Africa, training in sensor technologies, data analysis, and convergence science approaches, as well as exposure to interdisciplinary and applied research. The student will also develop practical skills in community engagement, co-design methods, monitoring and alert system development, evidence synthesis, and stakeholder engagement.

Co-supervision by international experts and close links with leading research, policy, practice organisations will equip the student with skills highly relevant for careers in academia, policy, international development, climate adaptation, and technology-driven sustainability research and solutions.

The supervisory team:

• Dr Kristine Belesova, Associate Professor in Global Population Health and Sustainability
• Dr Ali K. Yetisen, Associate Professor, Director of the Centre for Biochemical Sensors in the Department of Chemical Engineering
• Prof Firat Güder, Chief Engineer, Güder Research Group, Chair in Intelligent Interfaces, Department of Bioengineering, Imperial College London
• Dr Martina Anna Maggioni, Senior scientist at the Institute of Physiology, Center for Space Medicine and Extreme Environment Berlin, Group Leader of Climate Change and Health
• Dr Jayne Crozier, Senior Scientist and Team Leader of Trade and Commodities at Centre for Agriculture and Biosciences International Archives (CABI)
• Dr Zampela Pittaki, Soil Scientist at the World Agroforestry (ICRAF)

Lead supervisor’s contact details:

Dr Kristine Belesova, k.belesova@imperial.ac.uk

The textile industry has a large environmental footprint, especially in areas such as global warming, water depletion and land use, with the two leading textile materials, farmed cotton cellulose and petroleum derived polyester responsible for the majority of impacts. The industry suffers from high consumer demand and low recycling rates (<1% globally). Recycling textiles is challenging due to fibre degradation during use, high additive loadings, such as dyes, and the inability to process complex fibre blends (for example polycotton). Technological routes to reducing impact are developing recycling approaches that can mitigate for the challenges and reducing the impact of new fibre production. At the same time, large quantities of biomass waste with high cellulose content (~40%) such as agricultural by-products (wheat and rice straw) remain underutilised.

This project aims to propose an integrated and sustainable cellulose production and recycling system utilising diverse waste feedstocks, including polycotton textiles and waste biomass, to produce cellulose textiles with reduced environmental impact. Experimental, supply chain and process modelling approaches will be utilised to compare current and future textile fibre supply chains and select the least impactful option.

In the experimental part, low-cost ionic liquids will be applied to enable separation of cotton and polyester from polycotton fabrics and fractionation of underutilised agricultural crop residues to isolate a sustainable cellulose. Mild and selective electrobleaching will be investigated as a purification and decolourisation step for the cellulose recovered from both sources, targeting the removal of residual dyes from cotton-derived cellulose and residual lignin from wood-derived cellulose, to achieve industry standard cellulose properties. Alongside, energy efficient fibre welding will be explored as an advanced processing technique with low solvent consumption for damaged recycled cellulose fibres, enabling the conversion of the electrobleached cellulose into value-added products such as spun fibres that can be woven and non-woven materials.

Alongside the experimental effort, process modelling with life cycle assessment (LCA) and techno-economic analysis (TEA) will be used to identify bottlenecks in cost and environmental impact and enable comparison of different feedstocks and processing pathways. Key comparisons will be cotton textile fabrication and industrial Kraft pulping of wood to generate dissolving cellulose pulp followed by solution spinning of the wood cellulose using viscose and lyocell processing.

The student will carry out both experimental and modelling work to evaluate existing and emerging chemical processes with supply chains in mind, developing a unique and sought after skillset to efficiently develop more sustainable production methods. A background in chemistry or chemical engineering or related subjects is required as well as an interest in the bioeconomy, circular economy and policy.

Contact for further information:

Prof Agnieszka Brandt-Talbot, Department of Chemistry; agi@imperial.ac.uk

Prof Jason Hallett, Department of Chemical Engineering; j.hallett@imperial.ac.uk

Dr Andrea Bernardi; Department of Chemical Engineering;  a.bernardi13@imperial.ac.uk 

Applications are invited for a fully funded PhD studentship at Imperial College London on an interdisciplinary project spanning civil engineering, chemistry, and materials science.

Project Description:
Structural energy storage is an emerging field with the potential to transform sustainable infrastructure in which materials are designed to simultaneously perform mechanical and energy storage functions. While early research has focused on composites and cementitious systems, this PhD project will investigate a largely unexplored alternative: natural clay soils enhanced with conductive and electrochemically active phases.

Clay minerals possess charged nanoscale surfaces, rich pore-fluid chemistry, and unique physico‑chemical interactions. These characteristics make them a promising yet under‑studied platform for electrochemical energy storage. The aim of this PhD is to establish a fundamental, experimentally grounded understanding of how electrochemical functionality can be introduced into clay-rich geomaterials without compromising their mechanical performance in geotechnical 
applications.

Keywords: Sustainable infrastructure; Energy storage; Geomaterials; Electrochemistry; Geotechnical engineering

What the Student Will Do:
The PhD will combine laboratory experimentation, materials characterisation, and data analysis. The work will involve investigating the electrochemical behaviour of natural and engineered clays, performing electrochemical measurements alongside geotechnical and physical characterisation of the materials, and examining the interactions between mechanical behaviour and electrochemical processes. The project will focus on developing mechanistic understanding and transferable insights that can inform future large-scale or in-situ applications of clay-based energy storage.

Training and Benefits:
The studentship will provide (i) interdisciplinary training across engineering, chemistry, and materials science, (ii) hands-on experience with advanced geotechnical, electrochemical and material characterisation techniques and (iii) opportunities to collaborate across departments and research groups at Imperial College London.

Requirements:
• An Upper Second-Class Honours degree (or international equivalent) in civil 
engineering, materials science, chemistry, physics, or a related discipline
• A Masters level degree qualification, e.g. MSc/Meng/MPhil. 
• Excellent written and spoken English skills
• Strong interest in experimental research and interdisciplinary work

Funding:
The studentship covers tuition fees and living stipend for 3.5 years from October 2026.

This funding can be used to support an international student.

Supervision:
The selected candidate will join the Department of Civil and Environmental Engineering and work within the state-of-the-art Skempton Geotechnics Laboratory.

Supervision will be provided by

• Dr. Angela Casarella (Lead supervisor, Assistant Professor, Department of Civil and Environmental Engineering)
• Prof. Milo Shaffer (Professor, Department of Chemistry)
• Dr. Samuel Cooper (Associate Professor, Dyson School of Design Engineering)

How to apply:
Prospective applicants are encouraged to contact Dr Angela Casarella (a.casarella@imperial.ac.uk), Prof Milo Shaffer (m.shaffer@imperial.ac.uk) and Dr. Samuel Cooper (samuel.cooper@imperial.ac.uk) for further details, informal discussions and information about the project. 

With increasingly evident impacts of climate change, there is a growing need to improve how unstable coastal cliffs are characterised and monitored. Along the south coast of the UK, cliff failures are occurring under unprecedented conditions. In many cases, understanding and managing these hazards is limited by the inability to safely observe the condition of highly unstable slopes over time.

Conventional geotechnical investigation methods rely on intrusive testing and embedded sensors, which can be unsafe or impractical at actively failing cliffs. As a result, internal changes associated with drying, cracking, or progressive damage often remain unobserved, particularly under extreme conditions where changes can develop gradually before culminating in sudden failure.

This PhD project spans geotechnical engineering and applied particle physics to explore the use of muon tomography as a non-invasive method for subsurface characterisation and long-term monitoring of unstable cliffs. Muons are naturally occurring atmospheric particles capable of passing through tens of metres of rock or soil. By measuring how muons are attenuated or scattered, it is possible to infer internal density and structural anomalies without the need for drilling or embedding sensors.

Student activities

You will conduct lab experiments using muon detectors to study how different soil/rock samples influence measured responses. These experiments will examine sensitivity to material type, moisture, and internal structure and how changes can be detected over time. Building on the lab studies, you will use multi-detector muon tracking and machine learning, techniques routinely used in experimental particle physics, to explore how different materials and states can be distinguished beyond density, including sensitivity to atomic number and mineralogical traits. Lessons from the lab will be applied at an unstable coastal cliff in southern England, where you will test and refine these approaches in a real-world setting.

Student benefits

This studentship will offer training across geotechnical engineering, particle physics, and data science, providing hands-on experience with cutting-edge sensing technologies and field deployment. The student will develop skills in experimentation, machine learning, and interpretation of environmental data while collaborating with academic, industry, and public-sector partners. The project offers exposure to interdisciplinary research and real-world problem-solving, building expertise applicable across a wide range of challenges. The student will also interact with project partners from the World Heritage Organisation, local council authorities, and an industry partner with expertise in muon imaging.

Contacts: 

Lead supervisor:

Tiago Gaspar | Department of Civil and Environmental Engineering |  t.gaspar@imperial.ac.uk

Co-Supervisor

Nicholas Wardle | Department of Physics | n.wardle09@imperial.ac.uk

Nylon is everywhere—from clothing and carpets to engineering materials—yet its environmental footprint is enormous. Nearly 6 million tons of nylon are produced annually from fossil resources, generating around 10.5 kg of CO₂ per kg of nylon. With recycling rates below 5% and degradation times close to 40 years, discarded nylon accumulates in the environment, fragmenting into microplastics that pose serious risks to ecosystems, wildlife, and human health.

This PhD project addresses this challenge by redesigning nylon materials for increased sustainability. This requires of a multifaceted approach targeting their production and end of life management. Nylons are polyamides traditionally synthesised from oil-derived lactams, dicarboxylic acids, and diamines. Here, we propose a transformative alternative: using engineered microbes to produce nylon and nylon-like building blocks from renewable feedstocks. These microbial processes will generate both conventional monomers—yielding polymers identical to commercial nylon—and novel bacterial oligomers. Incorporating these bio-derived building blocks into polymer chains is expected to disrupt the hydrogen-bonding network that makes nylon highly resistant to degradation, enabling the creation of high-performance materials with enhanced biodegradability.

The specific objectives of this PhD are:

1-. Optimise fermentation strategies with engineered bacteria for the biosynthesis of nylon building blocks to obtain high titres suitable for nylon production.

2-.  Synthesise nylon materials using bio-based building blocks and in blends with commercial monomers in different proportions.

3-. Characterise the properties of the resulting materials (structural, tensile and water barrier) and assess biodegradability through enzymatic hydrolysis and soil-based microbial degradation studies.

The project will be supervised by a multidisciplinary team. Dr Jimenez is an expert in microbial engineering and bioproduction, with established methods for producing adipic acid, succinic acid, penta- and hexamethylenediamines, and other relevant molecules, as well as ongoing work on the evaluation of plastic biodegradation. Dr Romain specialises in the synthesis and characterisation of sustainable and smart polymers with enhanced biodegradability. Prof. Charalambides brings extensive expertise in polymer and materials characterisation, including diverse nylon formulations.

This highly interdisciplinary PhD sits at the interface of synthetic biology, polymer chemistry, and materials science, offering hands-on experience across the full lifecycle of sustainable plastics—from microbial fermentation to material synthesis, characterisation, and end-of-life assessment.

This project aims to deliver a platform for decarbonising the nylon industry by producing more sustainable, biodegradable nylon-like plastics, helping to redefine one of the world’s most important materials while providing training in cutting edge research with real-world impact.

Supervisors and contact emails:

Dr Jose Jimenez (Dept. of Life Sciences; j.jimenez@imperial.ac.uk)

Dr Charles Romain (Dept. of Chemistry; c.romain@imperial.ac.uk)

Dr Maria Charalambides (Dept. of Mechanical Engineering; m.charalambides@imperial.ac.uk)

Decarbonization and electrification are rapidly transforming electricity systems. Households are no longer passive consumers: electric vehicles, heat pumps, smart appliances, and automated controls mean that people now actively shape demand in real time. Yet most energy companies and system operators still rely on overly simplistic models of consumer behavior, overlooking learning, heterogeneity, automation, and adaptation. This mismatch creates a critical gap between how electricity systems are designed and how people behave -- undermining reliability, efficiency, and the success of net-zero transitions.

This PhD project aims to generate large-scale, real-world evidence on how households use electricity, respond to dynamic prices, and interact with automated technologies -- and to embed this evidence directly into the tools used to operate and design the electricity grid. During the project, there will be opportunities to interact and collaborate with industry partners in our network, including Octopus Energy.

The project has three tightly integrated components.

1. Dynamic Pricing and Demand Response.You will model how households respond to time-varying prices, capturing heterogeneity, adaptation, and learning over time, and building realistic demand models for policy and market design.
   
   
2. Decision Support for System Operation.You will translate behavioral models into tools for retailer operations (tariff design, hedging, and reserve procurement) and system planning (investment, capacity adequacy, and ancillary services), directly linking human behavior to optimization and control.
   
   
3. Automation in Household Response.You will study how automated control of appliances and heating systems changes price response and energy use and feed these insights back into behavioral models and system-level tools.

The project combines optimization, stochastic control, bilevel programming, and algorithmic methods with high-frequency market data and empirical modeling, ensuring behavioral evidence is usable for real-world decision-making under uncertainty.

What we offer
We offer a world-leading research environment with bright colleagues, vibrant seminars, close engagement with industry partners, and outstanding opportunities for collaboration and research stays with colleagues at Stanford, MIT, ETH Zürich, Oxford, and beyond.

You will gain deep expertise in optimization and decision-making under uncertainty, hands-on experience with large-scale energy data, a strong interdisciplinary profile, and excellent preparation for careers in academia, industry, policy, or system operation.

Supervisory team

• Dr Dario Paccagnan, Department of Computing
• Professor Ralf Martin, Imperial Business School (Economics and Public Policy)
• Dr Elina Spyrou, Department of Electrical and Electronic Engineering

Contact.
For further information, contact Dario Paccagnan at d.paccagnan@imperial.ac.uk using the subject line: PhD position SCS. To apply, include your CV, transcript of records, and a cover letter.

Robots operating in the real world must learn under severe constraints: a continuous stream of data from multiple on-board sensors, changing environments, and the persistent challenge of continual adaptation without catastrophic forgetting. Today’s dominant deep-learning pipelines typically assume abundant data and offline retraining, making them brittle and power-hungry for embedded deployment.

This interdisciplinary PhD will develop embodied neuromorphic AI by directly coupling heterogeneous robotic sensors (cameras, inertial sensors, odometry, etc.) to heterogeneous physical neural networks that learn rapidly from small datasets using rich, device-native nonlinear physical dynamics [1,2]. The project hinges on the complementary expertise of Dr. Jack Gartside (Physics), (PI, Neuromorphic Metamaterials group) developing programmable physical neural-network substrates and physics-aware learning algorithms that exploit nonlinear dynamics to rapidly learn from extremely scarce data, termed ‘few-shot learning’; and Prof. Andrew Davison (Computing), expert in real-time computer vision, developing 3D localisation, mapping and scene understanding algorithms for autonomous robotic systems, with a recent focus on efficient algorithm/processor co-design [3,4].

We will build a sensor-fusion neuromorphic stack that ingests multi-modal robot data into bespoke physical neural networks, enabling:

• Few-shot learning for new objects/scenes/tasks, enabling rapid generalisation to changing environments, for instance for safe indoor or outdoor navigation.
• Continual learning with reduced forgetting, allowing pre-trained models to adapt to new incoming data without loss of pre-existing information
• Rapid online adaptation of perception and control. Crucial for enabling the next-generation of autonomous robotics with embedded AI, harnessing nonlinear physical dynamics for learning which overcomes the limitations of conventional software AI when learning in data-scarce, continually changing environments.

The outcome will be a new class of embedded, low-power, adaptive robotic intelligence, validated on tasks in robotics and related embodied AI applications. The ability to learn rapidly in a device which is self-contained and running on low power may open up new applications for efficient robotics in applications like environmental monitoring, agriculture or construction.

[1] Gartside, Jack C., et al. "Reconfigurable training and reservoir computing in an artificial spin-vortex ice via spin-wave fingerprinting." Nature Nanotechnology 17.5 (2022): 460-469.

[2] Ng, Wai Kit, et al., & Gartside, Jack C. "Few-Shot Retinomorphic Vision in a Network Laser." under review, Science Advances (2025) [3] Murai, R., Dexheimer, E. and Davison, Andrew J. “MASt3R-SLAM: Real-Time Dense SLAM with 3D Reconstruction Priors”, CVPR 2025.

[4] Ortiz, J., et al., & Davison, Andrew J. “Bundle Adjustment on a Graph Processor”, CVPR 2020.

What will the student do and what benefits will it bring them?

This project will offer a student unique, multidisciplinary skills training across physics, computer science and robotics, and the opportunity to engage fully with expert research teams in these often-siloed areas.

- Ultra-fast optics and non-linear nanophotonic physics

- Unique physics-aware neuromorphic training algorithms

- Algorithms for probabilistics localisation, mapping and scene understanding

- Modern SLAM systems combining neural networks and state estimation and their implementation on real robot platforms.

Often neuromorphic and physical neural network research stops at the design and testing of the device on simple reference problems or benchmarks, and it never leaves the lab. For possibly the first time, in this project we will aim to integrate these devices with realistic robotic algorithms and competences for practical scene understanding and navigation.

A special focus will be to make use of physical neural networks to perform well in low data regimes, with low power, and with no connection to the cloud. In robotics, this opens up new directions in rapid scene understanding and the ability to react to ever-changing environments.

We welcome candidates from a diverse range of backgrounds with relevant technical experience, but for such an interdisciplinary project we recognise that no candidate will have all skills. We will provide close support and technical training. Most important is a willingness to learn and cross interdisciplinary boundaries.

Contact information:

Lead Supervisor: Professor Andrew Davison, Department of Computing, a.davison@imperial.ac.uk 

Co-supervisor: Dr Jack Gartside, Department of Physics, j.carter-gartside13@imperial.ac.uk 

Solving the world's most pressing challenges, from next-generation solar panels to targeted drug delivery, requires materials that don't currently exist. Crucially, functional technologies are rarely just a single molecule or a simple crystal; they are complex "hybrid" systems, such as organic-inorganic interfaces in batteries, nanoparticle catalysts, or nanospheres with inorganic shells for drug delivery systems.  However, current generative AI models in chemistry are modality-specific; capable of generating either small molecules or inorganic/molecular bulk crystals, but not both simultaneously. This limitation prevents the design of functional technologies for energy generation, health care, and catalysis.

This PhD project aims to break this barrier by building the first foundation generative model for the chemical sciences. Taking inspiration from AlphaFold 3’s impact on biology, you will develop a unified architecture capable of modelling interactions across disparate chemical domains. This will combine state-of-the-art continuous-time generative models (diffusion/flow-matching/flow maps) with high-throughput computational chemistry and experimental data. In addition, you will have the opportunity to investigate and develop novel inference-time techniques, such as steering/tilting and post-training with Chemistry-specific rewards and physical constraints. This project aims to develop a model that bridges the gap between discrete molecules and continuous crystalline lattices, and co-design using techniques in both discrete generative modelling, i.e., discrete diffusion, recursive language models, and continuous time frameworks.

What You Will Do

As a PhD student working on this project, you will sit at the interface of AI, Chemistry and Materials Science. Your primary responsibilities will include:

• Designing and implementing novel generative AI models, architectures, and inference techniques that can navigate complex chemical spaces.
• Building systems that learn from diverse multimodal data sources, ensuring the AI can generate materials that satisfy real-world experimental conditions.
• Working directly with experimental partners to validate your model's predictions, ensuring the materials you design can actually be synthesised.
• Contributing to the scientific community by releasing open-source models and datasets.

Benefits of the Studentship

• You will gain highly sought-after skills at the intersection of deep learning, computational chemistry, and materials science.
• You will be co-supervised by experts across three faculties (Chemistry, Computing, and Materials), providing a unique "convergence science" training environment.
• Your work will directly contribute to advancing artificial intelligence for sustainability, helping to engineer the materials required for a net-zero future.

Contact Details

For informal enquiries, please contact the supervisory team:

• Dr Alex Ganose, Department of Chemistry: a.ganose@imperial.ac.uk  
• Dr Joey Bose, Department of Computing: joey.bose@imperial.ac.uk  
• Dr Shelly Conroy, Department of Materials: m.conroy@imperial.ac.uk  

This project sits at the intersection of mathematics, statistics, machine learning, and cancer medicine, offering a unique opportunity for candidates with a quantitative background to tackle a real-world challenge in healthcare. The central aim is to develop and validate probabilistic deep learning models that estimate uncertainty in the delineation of tumour volumes and organs at risk for radiotherapy planning. Accurate delineation is crucial for effective cancer treatment, yet current approaches are limited by inter- and intra-observer variability and the impracticality of large-scale expert annotation.

An initial 3-month mini-project will focus on statistical analysis and modelling of clinician-defined ranges in cervical cancer imaging. The student will use existing datasets (CT and MRI, N=100) annotated with inner, reference, and outer contours of tumours and organs at risk to:

- Statistically characterize the variability and uncertainty in expert annotations.

- Implement and compare baseline segmentation models (e.g., deterministic U-Net vs. probabilistic U-Net) for uncertainty estimation.

- Quantify model performance using metrics such as Dice score, Hausdorff distance, and volumetric alignment.

This mini-project will provide hands-on experience in data analysis, statistical modelling, and machine learning, and will generate preliminary results that inform the design of more advanced probabilistic models. It will also allow the student to familiarise with medical imaging data and the clinical application of radiotherapy planning.

Building on the mini-project, the PhD will extend to the development of novel stochastic segmentation networks based on flow-matching (Flow-SSNs), capable of estimating high-rank pixel-wise covariances and generating multiple plausible contours. The student will explore annotation-efficient learning strategies, such as self-supervised learning and stochastic knowledge distillation, to address scalability and data efficiency. Integration of uncertainty estimates into adaptive radiotherapy planning will be a key translational focus, with comprehensive validation using retrospective and prospective clinical datasets. The project will culminate in the clinical implementation and evaluation of these methods, directly impacting patient care.

Candidates will apply mathematical rigor to real-world data, develop new algorithms, and collaborate with clinical and computational experts. The work is highly interdisciplinary, with clear pathways to impactful publications and career development in both academia and industry.

References

1. Bernstein et al., New target volume delineation and PTV strategies to further personalise radiotherapy, Physics in Medicine & Biology, 2021
2. Todd et al., Delineation Uncertainty from Clinician Ranges in Cervical Cancer Radiotherapy Planning, MICCAI CAPI Workshop, 2025
3. De Sousa Ribeiro et al., Flow Stochastic Segmentation Networks, ICCV, 2025

Supervisory team

• Professor Ben Glocker, Professor in Machine Learning for Imaging, Department of Computing
• Dr Wenjia Bai, Associate Professor in Artificial Intelligence in Medicine, Department of Brain Sciences
• Dr Alexandra Taylor, Consultant Clinical Oncologist, Royal Marsden Hospital
• Dr David Bernstein, Lead Physicist in Radiotherapy Imaging, Institute of Cancer Research

Contact

Prof Ben Glocker (b.glocker@imperial.ac.uk)

Humans learn to combine information from different senses—like sight and sound—through a long developmental process. As we grow, our brains reorganise themselves, gradually improving how we use multisensory information to understand the world. Most AI systems don’t learn this way: they are usually trained all at once on fixed datasets, without any of the gradual changes seen in real brain development.

This PhD project aims to explore what principles allow robust audiovisual perception to emerge. To do this, you will build computational models inspired by developmental learning and compare their behaviour with real brain data recorded across early life using high‑density Neuropixels technology (in collaboration with the Francis Crick Institute).

Aims

1. Explore how developmental training shapes learning. You will test how models behave when they are exposed to different patterns of sensory input over time—for example, starting with single‑sense (visual or auditory) learning and later adding combined audiovisual input. You will also examine how changes to the model’s structure (such as pruning or adding delays) affect how multisensory representations form.
2. Compare different types of learning rules. These will range from standard deep‑learning methods to more biologically inspired rules that approximate how real neurons adapt. The goal is to find which approaches lead to stable, generalisable, and noise‑robust multisensory processing.
3. Track how representations change over development. You will measure how internal neural codes in the models drift, align, or stabilise over time—and compare these patterns to longitudinal Neuropixels recordings from developing animals.

 Supervisory team and contacts

The primary supervisor and first point of contact is Dan Goodman (Imperial College Electrical Engineering, d.goodman@imperial.ac.uk). You can see more information about his recent research profile at his website https://neural-reckoning.org.

Co-supervisors are

• Marcus Ghosh (Imperial College Life Sciences, m.ghosh@imperial.ac.uk)
• Florencia Iacaruso (Francis Crick Institute, florencia.iacaruso@crick.ac.uk)

In your initial email, please include your current or expected UK-equivalent grades, and a couple of paragraphs explaining why you are interested in a PhD in this area. You can get a rough idea of UK grade equivalence via this link: https://www.grb.uk.com/recruiter-research/international-degree-equivalents

Overseas applicants can apply, but preference will be given to home students in the event of a tie. 

Transformer neural network models, based around attention and first proposed in the seminal paper Attention Is All You Need from Google researchers in 2017, are the key technical advance which has enabled the current explosion in AI. All cutting-edge AI models such as ChatGPT, Gemini, Llama etc. are based on this technology. However, these models, while extremely powerful, have billions or trillions of parameters and cost 10s of millions of dollars to train, as well as consuming astonishing amounts of power for training and inference. This blocks their direct use in EDGE devices, realtime remote components and wearable technologies as well as leading to huge environmental impact.

 We are looking to develop techniques and tools to shrink these models, while preserving high performance, to allow them to be used in a wider range of applications, importantly with far lower energy use.

 We were among the first in the world to demonstrate such architectures on FPGA (field programmable gate array) platforms in 2022 and remain world leaders in this area. Our work to date includes a recent contribution to Neurips (arXiv:2510.24784) and the best paper award at the FPT conference (arXiv:2508.15468). We showcased the multi-head attention mechanism in an FPGA chip with latency of O(100ns) and performed an initial exploration of the scaling behaviour of the design.

 We have also contributed to state-of-the-art optimisation tools and formed international partnerships, in particular with colleagues in the USA and CERN through the FastML collaboration and Altera UK. The work was done in the context of particle physics, which provides an excellent testbed for such work through its huge, highly structured and open datasets as well as direct scientific impact.

 We aim to recruit and train an excellent PhD student with a background in computing or physics to build on the initial work, focusing on challenges in particle physics. The research programme will explore an approach to produce high performance designs with high energy efficiency, and the capability of automating such designs. First, to study how the approach can cover physics applications and second, to extend the approach for healthcare applications such as adaptive radiotherapy, based on our existing collaboration with the Institute of Cancer Research.

Contacts:

• Professor Alex Tapper, Department of Physics (a.tapper@imperial.ac.uk)
• Professor Wayne Luk, Department of Computing (w.luk@imperial.ac.uk)
• Dr Hongxiang Fan, Department of Computing (hongxiang.fan@imperial.ac.uk)
• Professor Gavin Davies, Department of Physics (g.j.davies@imperial.ac.uk)

Plastic pollution is a major global challenge, with most environmental plastics originating from land-based sources. Over 2.5 billion people, primarily in the Global South, lack waste management services, resulting in widespread leakage of macroplastic waste. Lightweight packaging and single‑use plastics are easily transported by wind and runoff, moving through drainage, sewage, and river systems. Their small size and flexibility cause them to become trapped in natural features (vegetation) and built infrastructure, where they accumulate. Limited understanding of macroplastic transport across land–river systems prevents accurate source attribution and hinders effective mitigation.

A newly developed global, georeferenced inventory of plastic sources (Nature, 633:101–108, 2024) provides the basis for modelling plastic fate from land to sea. This PhD project will investigate post‑release behaviour using physics‑based simulations and digital twinning. It will analyse (i) interactions between macroplastics and terrestrial obstacles (e.g. roadsides, barriers, vegetation) and (ii) riverine features (e.g. bridges, groynes, culverts, meanders, vegetation) that control downstream transport. The work will also address poorly understood processes governing plastic transfer from land into freshwater systems.

The ideal candidate is an independent, innovative thinker with prior academic outputs, strong analytical skills, and enthusiasm for measurement, modelling, and quantitative environmental science. The PhD will focus on modelling and empirically quantifying environmental plastic movement by: (1) characterising common obstacles using theory and visual evidence; (2) defining realistic hydrodynamic and transport conditions (e.g. velocity, depth); and (3) integrating extensive field observations with numerical simulations (e.g. shallow water equations, discrete element methods). Ultimate, you are contributing to a novel emulator capable of operating at global assessment scales, extending a Beta model already created by Dr Velis’ team to estimate global over‑land plastic distribution.

The student will join an active research group specialising in plastic pollution, waste, resource recovery, and circularity, with opportunities for international collaboration. Publications in top academic journals are expected.

Contact the supervisory team

Lead Supervisor:

Dr. Costas Velis, Department of Civil and Environmental Engineering: c.velis@imperial.ac.uk

Co-supervisors:

Dr Adam Sykulski, Associate Professor and Reader in Statistics, Department of Mathematics, Faculty of Natural Sciences: adam.sykulski@imperial.ac.uk 

Dr Daniel Valero, Associate Professor, Fluid Mechanics Section, Department of Civil and Environmental Engineering: d.valero@imperial.ac.uk 

Lung cancer is the leading cause of cancer-related death globally, with 2.2 million new cases and 1.8 million deaths annually 1. While targeted therapies have improved outcomes, achieving >50% 5-year survival for some, clinical management remains complex, relying on histological classification and molecular profiling2. Crucially, a persistent 15%–20% of Non-Small Cell Lung Cancer (NSCLC) patients have no actionable mutations, and remain significantly lacking in targetable options. These limitations demand urgent, cost-effective identification of novel therapeutic targets. However, standard black box AI lacks the interpretability essential for clinical trust. Clinicians must understand why a specific biomarker is predicted by using AI tools. We propose a novel Agentic AI Framework where autonomous agents, orchestrated by Generative AI, reason across High-resolution CT (HRCT), genomics, and Whole Slide Imaging (WSI). By identifying and explaining emerging biomarkers like TROP2 QCS and MET fusions, this white-box tool enables verifiable, rapid patient stratification for precision oncology.

Aims and Objectives

Data Harmonisation: Curate a retrospective multimodal dataset from Royal Brompton and Harefield Hospitals, integrating WSI, HRCT, and reflex lung panel sequencing.

Agentic AI Development: Construct a multi-agent architecture where specialized agents collaborate via a central Generative AI reasoning engine to reach a consensus diagnosis.

Explainability & Trustworthiness: Ensure clinical transparency by implementing Chain-of-Thought prompting and attention mapping to generate human-readable explanations alongside predictions.

Biomarker Discovery & Translation: Validate the system on established predictive IHC and therapy response in NSCLC, subsequently transferring the architecture to Mesothelioma risk stratification.

Data & Methodology Leveraging the Royal Brompton and Harefield Hospitals’ status as a major referral centre, we will curate a retrospective, anonymized dataset of 10,000 lung cancer patients, integrating matched clinical data, reflex genomics, and WSI (H&E, Her2, PDL1) scanned at 40x magnification. All data will be anonymised and stored in compliance with GDPR guidelines.

We will:

Curate a massive harmonised dataset of 10,000 patients from a major referral centre to approximate foundation model scale.

Develop and fine-tune Vision-Language Models into specialist agents (Histology & Radiology) rather than training standard classifiers.

Deploy an LLM-based Supervisor Agent to synthesize findings, mimicking Multi-Disciplinary Team reasoning instead of traditional numerical fusion.

Construct Generative AI to output audit trails with natural language justifications and specific visual citations, directly addressing the black box trust gap.

Focus on predicting complex, emerging biomarkers to guide expensive antibody-drug conjugate therapies.

References 1 PMID: 37837979 2 PMID: 21252716

What the student will do |  Benefits of the studentship

The student will drive a transdisciplinary project at the intersection of Bioengineering, Computing, and Oncology to develop an Agentic AI framework that mimics a Multi-Disciplinary Team's reasoning for lung cancer biomarker discovery. By curating a massive multimodal dataset and engineering "white-box" foundation models , the student will learn to bridge the clinical trust gap through explainable AI techniques like Chain-of-Thought prompting. This studentship within the School of Convergence Science offers a transformative career path , providing the student with high-level expertise in trustworthy AI, deep clinical integration with partners like AstraZeneca and the NHS , and the leadership skills necessary to move healthcare from reactive care to proactive, data-driven precision medicine.

Contacts

• Dr Guang Yang, Department of Bioengineering (g.yang@imperial.ac.uk)
• Dr Jan Lukas Robertus, National Heart and Lung Institute (j.robertus@imperial.ac.uk)
• Professor Anil Bharath, Department of Bioengineering (a.bharath@imperial.ac.uk)

Most medical systems still treat disease as a binary variable: you either have a condition, or you don’t. In reality, health changes gradually. Long before a diagnosis is made, underlying biological, behavioural, environmental, and social processes may already be taking place in ways that are difficult to observe and are inherently uncertain.

This project takes a different perspective. Instead of asking whether someone has a disease, it asks where they are on an evolving health trajectory, how quickly that trajectory is changing, what future paths are possible, and when it makes sense to intervene. Clinical diagnoses and disease events are treated not as starting points, but as delayed and imperfect signals of the deeper underlying dynamics of health.

The aim of the project is to develop probabilistic machine learning models that can learn these hidden (“latent”) health trajectories from real-world longitudinal data. By combining information collected over time — both inside and outside traditional clinical settings — these models seek to infer an individual’s current health state, the rate at which it is changing, and quantify uncertainty. This may make it possible to reason more carefully about early intervention, prevention, and decision-making before people become patients.

Methodologically, the project uses tools from statistics, machine learning, and applied mathematics, including Bayesian inference, stochastic processes, and state-space models. These approaches are particularly well suited to real health data, which are often noisy, incomplete, and irregularly sampled. Health “position” represents an underlying level of burden or resilience, while health “velocity” captures whether someone is improving, stable, or deteriorating over time — even if their current measurements look similar to someone else’s.

The models integrate diverse data sources such as clinical metrics, physiological signals, behavioural indicators, and socio-demographic context, while explicitly accounting for uncertainty. A key focus is learning from low-burden, real-world data, including passively collected or intermittently observed information.

As a concrete example, in cardiometabolic health the approach could distinguish between two people with similar blood test results today, but very different underlying trajectories — one stable and one rapidly worsening — implying very different intervention needs.

Overall, the project contributes new ways of thinking about health as a dynamic, uncertain process. It aims to support earlier, fairer, and more effective prevention strategies, bridging data science, health research, and social decision-making.

What the student will do

The chosen PhD candidate will work at the convergence of data science, health research, and social science. They will design and implement probabilistic machine learning models to study how health evolves over time using real-world longitudinal data. This will involve working with complex datasets, developing and testing statistical models, and exploring how uncertainty in health trajectories affects decisions about prevention and intervention.

The student will collaborate with researchers across disciplines, including statistics, machine learning, public health, and social science, and will have opportunities to apply their work to concrete health challenges such as cardiometabolic disease. Alongside technical development, the student will engage with broader questions about decision-making under uncertainty, prevention, and the design of health systems.

Benefits for the student

The studentship will provide advanced training in modern statistical modelling, Bayesian methods, and machine learning, with a strong emphasis on working with real, imperfect data. The student will develop highly transferable skills in quantitative analysis, computational modelling, and interdisciplinary research.

Beyond technical skills, the project offers experience in framing complex societal problems, communicating results to diverse audiences, and working across disciplinary boundaries. This combination prepares the student for a wide range of careers, including academic research, data science, health policy, industry, and public sector roles focused on health, analytics, and decision-making.

Contacts

Lead supervisor

Professor Mauricio Barahona
Department of Mathematics
Imperial College London
Email: m.barahona@imperial.ac.uk

Co-supervisors

Professor Franco Sassi
Department of Economics & Public Policy
Imperial Business School
Email: f.sassi@imperial.ac.uk

Dr Reza Skandari
Department of Analytics, Marketing and Operations
Imperial Business School
Email: r.skandari@imperial.ac.uk

Administrative contact

Jack Olney
Executive Director, Centre for Health Economics & Policy Innovation (CHEPI)
Imperial Business School
Email: jack.olney@imperial.ac.uk

Across Sub-Saharan Africa, millions of smallholder farmers are increasingly exposed to dangerous heat. Agriculture in the region relies heavily on manual labour, and rising temperatures are increasing the risk of heat-related illness, injury, and reduced ability to work. These effects extend beyond physical health, threatening livelihoods, wellbeing, food security, and national economies. Heat-related productivity losses in agriculture are estimated to have already reduced Africa’s GDP by around 4%.

This PhD project addresses heat-related health and productivity challenges by combining two promising approaches: agroforestry and sensor technology. Agroforestry—the integration of trees into farming systems—is a widely promoted climate adaptation and mitigation strategy, with about 7% of Africa’s land committed under climate policies. Trees can reduce local temperatures by up to 6°C, yet their direct benefits for farmers’ health and productivity remain poorly understood. At the same time, wearable health devices and environmental sensors are increasingly used to monitor heat stress in athletes and workers, but their application in smallholder farming, particularly in low-income settings, is still limited.

The project aims to develop and test a low-cost system that integrates health, productivity, and environmental monitoring to assess whether agroforestry and real-time heat alerts can help farmers work more safely in hot conditions while maintaining productivity. The student will complete three components. First, they will develop a practical monitoring and alert framework based on a reviewing relevant literature alongside input from experts across relevant disciplines. Second, they will adapt and pilot the framework in a real-world African setting, comparing farmers working in agroforestry and non-agroforestry systems. They will collect data on physiological responses to heat (such as heart rate, body temperature, hydration, and energy use), productivity (including movement patterns, time spent working, and work output), and environmental conditions. Third, the student will use participatory engagement and co-design with farmers, other end users, and experts to identify needs, constraints, and opportunities for future development of affordable, practical, scalable, and context-appropriate sensor-based heat adaptation tools and smart agroforestry practices.

The study will take place at an established agroforestry research site in Kenya or Zambia. The student will be supported by a multidisciplinary supervisory team with expertise in heat physiology, health and environmental sensors, environmental epidemiology, farming practices and work behaviour, agroforestry and its microclimate, alongside strong regional and local partnerships through organisations promoting agroforestry practice and resilient agriculture locally and across the African region.

What the student will do and benefits of the studentship

This project builds on the disciplines of engineering, climate change, environmental and occupational health, and international development.

The PhD student will design and test a low-cost monitoring and alert system to investigate how heat affects farmers’ health and productivity, and how agroforestry can reduce these risks. They will work with wearable health sensors, environmental monitoring tools, and field-based measures of labour productivity, carrying out interdisciplinary research at the interface of environmental science, health, and agriculture. The student will also work closely with farmers and practitioners through participatory research and co-design activities.

Through this studentship, the student will develop a strong foundation in climate change and health, agroforestry and sensor-based approaches to climate adaptation. They will gain specialist expertise in assessing heat stress impacts on health and labour productivity, and in applying wearable and environmental sensors in real-world settings. The project will provide hands-on experience of field research in Africa, training in sensor technologies, data analysis, and convergence science approaches, as well as exposure to interdisciplinary and applied research. The student will also develop practical skills in community engagement, co-design methods, monitoring and alert system development, evidence synthesis, and stakeholder engagement.

Co-supervision by international experts and close links with leading research, policy, practice organisations will equip the student with skills highly relevant for careers in academia, policy, international development, climate adaptation, and technology-driven sustainability research and solutions.

The supervisory team:

• Dr Kristine Belesova, Associate Professor in Global Population Health and Sustainability
• Dr Ali K. Yetisen, Associate Professor, Director of the Centre for Biochemical Sensors in the Department of Chemical Engineering
• Prof Firat Güder, Chief Engineer, Güder Research Group, Chair in Intelligent Interfaces, Department of Bioengineering, Imperial College London
• Dr Martina Anna Maggioni, Senior scientist at the Institute of Physiology, Center for Space Medicine and Extreme Environment Berlin, Group Leader of Climate Change and Health
• Dr Jayne Crozier, Senior Scientist and Team Leader of Trade and Commodities at Centre for Agriculture and Biosciences International Archives (CABI)
• Dr Zampela Pittaki, Soil Scientist at the World Agroforestry (ICRAF)

Lead supervisor’s contact details:

Dr Kristine Belesova, k.belesova@imperial.ac.uk

Digital wellbeing apps like just-in-time adaptive interventions (JITAIs) promise to ‘nudge’ people toward more healthy digital media use, based on data from their devices. Yet most existing apps rely on sparse, delayed data from a single platform and are not engaging to use.

In this PhD, you will design, develop, and evaluate an engaging, open-source, privacy-preserving JITAI that focuses on video games and emotion regulation among adolescents. Building on techniques developed by the supervisors, you will develop and test a system that collects behavioural data on gameplay across console, PC, and smartphone platforms, combined with smartphone-based momentary measures of affect and motivation. 

The project is embedded in established clinical and research partnerships at Imperial for recruitment and impact, including the West London Mental Health NHS Foundation Trust and Smart Data Donation Service.

Supervisors

• Prof Sebastian Deterding (primary supervisor, Dyson School of Design Engineering)
• Dr Martina Di Simplico (Department of Brain Sciences)
• Dr Nick Ballou (Dyson School of Design Engineering)

What you will do

• Design, build, test data collection software
• Integrate and analyse multi-platform digital behavioural data
• Develop and test a smartphone digital wellbeing intervention
• Work with adolescents, parents, and schools
• Publish transparent, reproducible research

The ideal candidate brings …

• Distinction/First Master’s degree in a relevant field (e.g. computational social science, HCI, psychology), or UK 2:1 with strong research/industry experience
• Strong programming skills (Python, R, and/or JavaScript)
• Experience with open science practices
• Ability to communicate clearly with adolescents, parents, and schools
• English proficiency
• International students welcome; funding covers home fees only.

What you will gain

• 3.5 years fully funded (home tuition fees + stipend at UKRI rates, £23,195 p.a. 2026/27)
• Research budget (£4,000 total)
• Make a practical contribution to adolescent digital wellbeing
• Gain expertise in real-world behavioural measurement with policy relevance
• Work at the intersection of data science, mental health, and user-centred design

How to apply

Before you apply, email s.deterding@imperial.ac.uk (CV attached, subject “SoCS studentship”) to arrange an informal conversation

Sensor-driven, agentic early‑warning systems offer important opportunities for anticipating dengue virus epidemics in a changing climate.

This project aims to drive entirely new surveillance systems for climate and disease risks, building a low-cost surveillance swarm of ground weather sensors, that optimize and adapt under active-learning policies, and integrate AI-agentic decision support to predict arbovirus transmission risk and support targeted resource allocation.

Our team has built a strong collaborative ecosystem, the global DeZi network, united by a commitment to equitable innovation and actionable global‑health impact. This PhD project is a direct continuation of that trajectory, co‑developed with partners in Brazil and Angola.

You will deploy small solar‑powered weather sensors across sites in Brazil and Angola. These devices record high‑frequency environmental data that help characterise fine‑scale conditions relevant to transmission. You will integrate these data with satellite climate indicators and routine dengue surveillance in an intranet-of-sensors to quantify the added value of local microclimate measurements.

You will then use multi-loss active-learning policies to optimise sensor placement and generate dynamic transmission‑risk thresholds that adapt to climate anomalies, shifting vector habitats and operational constraints.

Finally, you will build an agentic AI layer that monitors network performance, detects model drift, identifies supply‑chain pressures, and proposes relocation of sensors, model retraining, and reallocation of diagnostics under real‑world data and healthcare resource constraints. This will transform the EWS into an intelligent decision‑support system capable of functioning under incomplete or noisy data.

Training Benefits

This PhD offers solving a real‑world surveillance challenge, immerse in day-to-day technical and AI challenges, and learn from diverse scientific perspectives. You will gain hands‑on experience in developing a real-world AI‑enabled decision‑support tool, from building and deploying IoT devices for environmental data collection, training in adaptive learning and statistical machine learning, to developing the agentic decision‑support language model.

Our team spans multiple departments and disciplines – School of Public Health, Department of Mathematics, Department of Life Sciences – and includes partners at key institutions in Brazil, Angola and Singapore.

There will be further opportunities to benefit from PhD-level training through Imperial’s Centres for Doctoral Training in the Department of Mathematics, including the Mathematics for our Future Climate CDT & Statistics & Machine Learning StatML CDT, and you will work closely within a supportive and agile global health research setting, the Dengue and Zika Immunology and Genomics Multi‑Country Network.

Together, this will provide an intellectually rich environment of convergent sciences, integrating engineering, statistical machine learning, AI, and global health innovation.

Contacts

Lead:

• Prof. Nuno R. Faria, School of Public Health | nfaria@ic.ac.uk 

Co-supervisors:

• Dr Oliver Ratmann, Department of Mathematics | oliver.ratmann05@imperial.ac.uk 
• Prof. Cristina Banks-Leite, Department of Life Sciences | c.banks@imperial.ac.uk 
• Dr Swapnil Mishra, Saw Swee Hock School of Public Health, National 
  University of Singapore | swapnil.mishra@nus.edu.sg 

Irregular heart rhythms (cardiac arrhythmias) are common, leading to serious complications such as stroke, yet many episodes are never detected as current monitoring devices (wearables) are uncomfortable, expensive, or impractical for long‑term use at‑home. Conventional wearables are often rigid, need frequent charging, and may perform poorly in everyday lighting conditions, which limits patient adherence and real‑world effectiveness. This project will develop flexible thin‑film photodetectors (PDs) based on organic and perovskite semiconductors to enable comfortable, continuous cardiac monitoring using light. These devices measure tiny changes in blood volume at the skin surface (photoplethysmography), providing information related to heart rhythm and cardiovascular status. Thin‑film technologies offer unique advantages for this application: they can bend and conform to the body, be manufactured at low temperature on plastic substrates, and their optical response can be tuned to target specific wavelengths that are sensitive to oxygenated and deoxygenated blood. By integrating light‑harvesting thin‑film solar cells, the devices aim to operate using ambient indoor light, reducing or eliminating the need for batteries and frequent recharging. The project is highly interdisciplinary, sitting at the interface of chemistry, materials engineering, device physics, and digital health. You will work with a supervisory team spanning thin‑film materials and processing, device fabrication and characterisation, and data‑driven health technologies. The team also collaborates with clinical and industrial partners, ensuring that device performance targets, form factors, and data outputs are relevant for future translation into healthcare settings.

Over the three‑year programme you will:

• Design and fabricate organic and perovskite thin‑film PDs and small integrated modules for cardiac‑relevant optical sensing.
• Optimise device performance under realistic indoor lighting.
• Explore scalable fabrication routes and implementation in real-world monitoring systems.
• Collaborate with digital health colleagues to understand basic signal acquisition and how these devices could fit into future home monitoring systems.

This studentship will deliver integrated training in functional materials synthesis/processing, cleanroom/device fabrication, optoelectronic and stability testing, and basic data analysis for physiological signals. You will gain experience of working in an interdisciplinary environment and interacting with external partners, providing an excellent platform for careers in academia, MedTech, or the wider digital health and sensor industries.

Contacts

Lead supervisor: Dr Nicola Gasparini, Department of Chemistry: n.gasparini@imperial.ac.uk 

Co‑supervisor: Professor Martyn McLachlan, Department of Materials: martyn.mclachlan@imperial.ac.uk 

Traumatic brain injury (TBI) affects an estimated 50–60 million people worldwide each year and can have life-altering consequences. Despite its prevalence, we lack reliable tools to predict when real-world head impacts will result in brain injury, limiting our ability to identify high-risk events and develop effective prevention strategies in sport, transport, and defence.

Recent advances create a unique opportunity to address this challenge. Wearable technologies such as instrumented mouthguards now enable measurement of head motion during real-world impacts. Blood-based biomarkers provide objective indicators of brain injury, while modern computational models can simulate how the brain deforms under mechanical loading. However, these approaches are typically studied in isolation, even though injury arises from the interaction between mechanical forces and brain biology.

This PhD will integrate engineering, neuroscience, and data science to develop biologically validated, multi-scale models of white matter injury that explicitly account for axonal structure. The project builds on a large ongoing study of elite players (female and male), combining head kinematics, blood biomarkers, and clinical outcomes. This dataset allows mechanical predictions of brain deformation to be directly tested against objective biological measures.

The student will incorporate axonal fibre orientation into Imperial College high-resolution computational brain model to predict tissue strain along and across axonal pathways. Regions experiencing elevated strain will be examined using micro-scale models to capture local injury mechanisms. These simulations will be driven by real-world head impact data, and their outputs will be linked to biomarker and clinical measures using statistical and machine-learning approaches to identify injury patterns and risk thresholds.

Although elite sport serves as a controlled testbed, the methods developed will be designed to translate to broader contexts, including community sport, road traffic collisions, and military exposure, supporting scalable approaches to brain injury prevention.

The student will gain interdisciplinary training in computational modelling, biomedical data analysis, and translational research while working closely with engineers, neuroscientists, clinicians, and sporting bodies.

Benefits to the student

This studentship offers interdisciplinary training at the interface of engineering, neuroscience, and data science. The student will gain experience with state-of-the-art computational modelling, real-world biomedical datasets, and translational research with direct societal impact. They will be embedded in a strong academic–clinical–industry network, develop highly transferable technical and analytical skills, and contribute to research that informs injury prevention and brain health policy.

Who should apply?

This project is suitable for candidates with a strong background in engineering or the physical sciences (e.g. mechanical, bioengineering, aerospace engineering, physics, applied mathematics, or related disciplines). Applicants should have a master’s degree with high marks overall and in relevant subjects. Applicants should be motivated by applied biomedical research, comfortable working with quantitative data and computational tools, and enthusiastic about learning new methods across disciplines. As the project involves close collaboration with clinicians, neuroscientists, and external partners, excellent teamwork and communication skills are essential. Curiosity, initiative, and a willingness to engage with both engineering and biological perspectives will be key to success.

Supervisory Team

• Dr Mazdak Ghajari, Dyson School of Design Engineering
• Professor David Sharp, Department of Brain Sciences
• Dr Tom Parker, Department of Brain Sciences

How to apply

Please send your CV, personal statement, transcripts and two reference letters to Dr Mazdak Ghajari at m.ghajari@imperial.ac.uk. The application deadline is 25^th February 2026.

Burnout amongst the workforce is prevalent in the modern workplace and, for those working in safety-of-life sectors, potentially life-threatening. In the medical and emergency responder domains, various reports have highlighted the impacts of burnout on General Practitioners (GPs) and firefighting personnel. A clear finding from these domains is that burnout can contribute to misdiagnoses in the former and mistakes in the latter ultimately leading to fatalities. Evidence shows that an important driver of burnout is task complexity when staff operate under time pressure and high cognitive load.

This studentship aims to identify the appropriate combination of methods for measuring cognitive load during clinical diagnosis (GPs) and situation assessment in firefighting, and to anticipate error. It will use wearable technologies in real time operations to develop interventions for GPs and firefighters based upon psychophysiological markers of cognitive fatigue and develop recommendations for intervention strategies. 

For GPs, the studentship will analyse the impact of AI-powered, Ambient Voice Technologies (‘digital scribes’) on GPs’ cognitive load as part of a CRUK project (PI: Kostopoulou, co-I: Majumdar, 2026-2029). This project will conduct behavioural experiments with GPs in simulated consultations. The student will measure the GPs’ cognitive load during the experiment and will have access to an unprecedented multimodal database of videos and conversational transcripts of clinical consultations, to complement the analyses. In the case of firefighters, cognitive load will be measured in virtual reality scenarios developed by Imperial’s Digital Media lab.

The student will use minimally invasive, neurophysiological methods for measuring cognitive load, for example, heart rate, heart rate variability, and pupil dilation, and will complement these methods with self-report measures of workload. The student will also assess the impact of experience on cognitive effort under different conditions. By developing minimally invasive and transferable methodologies to be deployed when testing the impact of decision support and AI on the decision making of safety-critical personnel in their operating environment, this studentship aims to critically assess the link between burnout, diagnostic errors, and failures of situation assessment; and to develop recommendations for averting burnout in safety critical domains.

Beyond familiarisation with neurophysiological measures of cognitive load, the student will learn to design and conduct experiments with human subjects, test theories of cognition and decision making in naturalistic environments, and understand the opportunities and constraints of conducting research with experienced professionals. The student will also have the opportunity to conduct research in different operational communities and develop occupational health and safety policies.

Contact: 

Professor Arnab Majumdar, Department of Civil and Environmental Engineering; a.majumdar@imperial.ac.uk

Dr Olga Kostopoulou, Department of Surgery and Cancer; o.kostopoulou@imperial.ac.uk 

Humanitarian medical teams frequently need to make operational decisions on a horizon of days: how to staff facilities, position supplies, anticipate surges in trauma cases, and adapt referral pathways when violence patterns and access to care are changing rapidly. Yet most quantitative forecasting tools either focus on long-term strategic trends or treat conflict dynamics and healthcare demand as separate problems.

This PhD will develop new probabilistic, short-horizon forecasting methods that explicitly link spatio-temporal conflict events, mobility constraints, and hospital admissions. The project sits within Imperial’s JI-RISE programme, which develops quantitative tools for crisis response and resilience, and builds on an established collaboration with Médecins Sans Frontières (MSF). Anonymised facility-level incidence and typology data are available under appropriate data governance and ethical approval.

The methodological core of the project is a road-network-constrained spatio-temporal point process model (Hawkes-type) for conflict events. Rather than assuming events spread in straight-line (Euclidean) space, propagation will be defined over travel time along road networks and accessibility surfaces. This allows forecasts to reflect realistic movement corridors, barriers, and disrupted access. The model will separate baseline spatial risk from short-term self-excitation dynamics and produce calibrated probabilistic forecasts with explicit uncertainty quantification. Structured AI extensions may also be explored, such as graph-based representations of road networks or neural parameterisations of mobility-constrained kernels, provided interpretability and robustness are preserved.

In a second stage, these conflict forecasts will be translated into facility-level predictions of hospital admissions and patient case mix. The model will ingest predicted conflict intensity, accessibility indicators, and recent reporting to generate short-term probabilistic forecasts of admissions. Uncertainty from the conflict model will be propagated through to downstream predictions. The framework will also account for dynamic catchment effects, recognising that similar levels of violence may generate very different facility demand depending on mobility constraints and displacement patterns.

Supervisory Team

• Dr Oliver Watson (primary supervisor, School of Public Health)
• Dr Ed Cohen (Department of Mathematics)
• Dr Elizaveta Semenova (School of Public Health)
• Prof Bhargavi Rao (Médecins Sans Frontières)

What you will do

• Build reproducible geospatial data pipelines and road-network substrates.
• Implement and compare parametric and structured AI extensions of Hawkes-type models.
• Develop and validate simulation frameworks for stress testing and benchmarking.
• Conduct rolling backtests and calibration analyses on real and synthetic data.
• Produce open, well-documented research software and peer-reviewed publications.

Training and development

This studentship provides interdisciplinary training in probabilistic machine learning, spatio-temporal statistics, network modelling, and research software engineering. You will work within the JI-RISE programme, engaging with researchers across public health, mathematics, and engineering. Through collaboration with MSF operational partners, you will gain experience translating quantitative research into tools that inform real-world humanitarian policy and decision-making.

This project is suitable for candidates with a strong quantitative background (Distinction/First Master’s degree in statistics, mathematics, machine learning, physics, engineering, or computer science), experience in Python, English proficiency. Above all, applicants should be motivated by using advances in AI and machine learning to address complex real-world problems, particularly in settings where robust analytical tools are most needed.

How to apply

Before you apply, email o.watson15@imperial.ac.uk (CV attached, subject “SCS studentship inquiry”) to discuss further.