Our MRes AI ML promotes transdisciplinary excellence

Contact details

For further enquiries about the MRes in AI and Machine Learning programme, please email us: doc-mresadmissions@imperial.ac.uk

We are excited to offer this new research training programme for graduates who want to become Artificial Intelligence researchers and innovators. If you are enthusiastic to become an Artificial Intelligence researcher and innovator who would want to learn to develop novel and innovative AI for a custom problem, then this programme is for you.

With artificial intelligence now embedded in many areas of business and public service, people are needed who combine theoretical grounding in AI with the ability to imagine, lead and deliver Research & Development (R&D) projects that meet regulatory and real-world performance expectations. This MRes programme gives the opportunity to gain these skills, for a wide range of industries and careers, and we offer projects in various domains such as health, business and finance, communications, and energy / product supply systems.

The MRes programme is a one year full-time programme leading to th MRes award. It is designed to provide focussed AI training and a high-level supervised research project that will result in developing high-level analytical skills and a broad range of competences. The MRes also involves taught-module lectures and assessments in the first and second terms, combined with initial work on the main project, followed by full-time work on the project for the remainder of the year, with submission of an individual thesis at the end. The research project is normally supervised by one AI expert, and often co-supervised with an expert in an application area.  Some students may also have a co-supervisor from industry. MRes award holders would be able to, for example:

  • Apply broad knowledge of state of the art AI and machine learning to critically assess the strengths and weaknesses of a range of research and innovation approaches. 
  • Apply the principles of the law as well as understanding of responsible research and innovation, data protection, ethics and bias relevant to AI research and innovation. 
  • Create software for advanced AI and machine learning using appropriate computing languages (e.g. Python) and frameworks (e.g. PyTorch, Tensorflow). 
  • Identify key advances, uncertainties and opportunities in AI methods and the evidence on organisational, business and human factors for applications.

Covid 19 information for prospective new students

As a result of the COVID-19 pandemic, and to ensure that your MRes programme can be delivered safely, we are including this information about how we intend to deliver your programme. Studying during these uncertain times could be particularly challenging, and we want our students to be reassured that we have taken measures to both ensure that Imperial is a safe place to study and carry out research and enhance motivation and productivity.

We remain careful in how we deliver large scale teaching, research, or social events, with online provision remaining as an option. We would aim to offer a combination of on-campus and remote activities for research degrees that seek to maximize physical access and in person engagement within given health and safety restrictions.

We are looking forward to welcoming you on campus, unless we are prevented from doing so by UK Government restrictions.

For more information about Imperial’s programmes’ delivery, your learning experience and the steps planned to keep you safe on campus if you are able to join us, please see our Covid 19 information for applicants and offer holders page.

Work in progress....

How do I apply?

Overview
We are inviting applications from students who are enthusiastic and strongly motivated by research and development work in AI methods and/or AI applications for various areas of business and public services in a multidisciplinary setting and welcome students from all over the world and consider all applicants on an individual basis. 

The Department values diversity and equality and is committed to providing an inclusive environment in which all students can thrive, and we particularly encourage applications from women, disabled and Black, Asian and Minority Ethnic applicants, who are currently under-represented in the sector. Our students will be registered in the Department of Computing, which is also an Athena SWAN Silver Award winner, a Stonewall Diversity Champion, a Two Ticks Employer, and is working in partnership with GIRES to promote respect for trans people.

The provisional application deadline is ca early August 2021 (for international applicants).
Application deadlines for Home/UK applicants might be different and will be confirmed shortly.

Early applications are encouraged; there is no guarantee that projects and places will be available until the application deadline.

Only students who have been matched with a project and have met all relevant requirements, would be able to be considered for an offer. Please also note that admissions to the programme is very competitive and meeting the minimum entry requirements does not guarantee that an offer could be made.

Entry requirements
The minimum academic requirement is normally a first-class degree in a relevant scientific or technical discipline, such as computer science, engineering, mathematics, statistics or physics. Graduates with other natural science degrees (e.g. life sciences, chemistry) who have strong mathematical or computing grounding and programming skills may also be considered.

If you have a non-UK degree, please check the College minimum entry requirements, but note that the entry requirements to this MRes programme are normally higher than College minimum requirements.

Non-native English speakers must also meet the College English requirements. You do not have to take a test before applying, but need to pass an approved test before starting the programme.

Apply online
Please apply online via our MRes AI and Machine Learning page of the Imperial postgraduate prospectus.

Before applying, you should have contacted your preferred supervisor(s) directly to discuss opportunities and potential projects. Please visit our Projects page below to choose a project OR, if none of the advertised projects is suitable for you, please read the guidance on how to go about being considered by other supervisors or for other projects. If you are interested in more than one project, please tell us your order of preference.

What happens after I apply?
Your application will be reviewed by the MRes Programme Admissions tutor and the proposed MRes project supervisor(s). If the Project supervisor(s) want to take your application forward, you will be invited for an interview. Applicants are also normally required to complete our MRes admissions tests. 

The outcome of your application is communicated via the College application system. If you are offered a place, you will need to fulfil any required conditions, shown in their application portal, such as for example the English language test, references, certificate of academic qualification, ATAS etc before you can start the programme.

Projects

As part of the application process, students typically discuss possible projects with potential supervisor(s) before making their MRes application to ensure that there is a suitable project available. 

Below please find a list of MRes project examples. Please contact the project supervisors for more information and/or to indicate your interest. Additional supervisors and projects opportunities might be available. To find out more, please go to this webpage to help you decide on which supervisor(s) you may wish to contact.

Examples of MRes projects 
Project titleProject supervisor and contact email
An Information-Theoretic view of Batch Normalisation Li, Yingzhen 
yingzhen.li@imperial.ac.uk
CNN-based image analysis for automatic quantification of fungal burden in histology images Tanaka, Reiko
r.tanaka@imperial.ac.uk
Coupled Super-Resolution and Blind Denoising for Multi-sequence CMR Yang, Guang
g.yang@imperial.ac.uk
CU2: A “clinically usable and useful” deep-learning toolbox for scoring and monitoring chronic lung disease on CT scans Angelini, Elsa
e.angelini@imperial.ac.uk
Data efficient and generalisable machine learning for metastasis detection on whole-body MRI scans Bai, Wenjia and Rockall, Andrea
w.bai@imperial.ac.uk and a.rockall@imperial.ac.uk
Deep learning-based hyperspectral unmixing for Raman biomolecular imaging Barahona, Mauricio
m.barahona@imperial.ac.uk
Generalisability of Clinical Prediction Models Across Heterogeneous Populations Parbhoo, Sonali
doc-mresadmissions@imperial.ac.uk
Geometric deep learning for high-fidelity peptide sequence determination by two-dimensional partial covariance mass spectrometry Bronstein, Michael
m.bronstein@imperial.ac.uk
Human Validation for Off-Policy Evaluation and Optimisation for Renal Replacement Therapy Parbhoo, Sonali
doc-mresadmissions@imperial.ac.uk
Image-to-image translation to remove skin colour bias in eczema severity scoring Tanaka, Reiko
r.tanaka@imperial.ac.uk
Infinite Width Deep Neural Networks: Analysis and Improvements van der Wilk, Mark
m.vdwilk@imperial.ac.uk
Interpretable prediction of minimum inhibitory concentrations of antibiotics in bacterial pathogens from whole-genome sequencing data Chindelevitch, Leonid
l.chindelevitch@imperial.ac.uk
Invariance Learning in DNNs through (PAC-)Bayesian Methods van der Wilk, Mark
m.vdwilk@imperial.ac.uk
Reinforcement Learning from Demonstrations for Robotics Johns, Edward
e.johns@imperial.ac.uk
Robust Implementation of Modern Gaussian Processes van der Wilk, Mark
m.vdwilk@imperial.ac.uk
Single Breath-Hold Diffusion Tensor Cardiac MR Using Advanced Generative Adversarial Networks Yang, Guang
g.yang@imperial.ac.uk
Solving Optimisation Issues in Deep Gaussian Processes van der Wilk, Mark
m.vdwilk@imperial.ac.uk
Unsupervised discovery of robust robotics behaviours using deep learning and evolutionary algorithms Cully, Antoine
a.cully@imperial.ac.uk
Weakly Self-supervised Deep Learning for COVID-19 Prognosis Yang, Guang
g.yang@imperial.ac.uk
   
If you have questions, please do not hesitate to contact us at doc-mresadmissions@imperial.ac.uk
Summary of the table's contents

What will I study?

Core to our MRes programme is the research thesis, a substantial and major project providing an opportunity for students to apply a systematic approach to solving a substantial problem in or with AI. Furthermore, the MRes programme includes AI foundation courses, including ethics, responsible innovation and research skills training, including literature review and simulated R&D project proposal.

The programme would normally include: 
a. AI foundation courses, including responsible innovation and ethics
b. Research skills training, including literature review and simulated R&D project proposal
c. A major independent research project and thesis

a. The AI Foundation courses will strengthen your skills in programming and AI technologies.  You will master programming for advanced AI (e.g. Python programming) and machine learning; will have taught modules in law and ethics, and a two-term series of tutorials (reading group model) looking at a wide range of state of the art AI algorithms and applications beyond your project domain.

b. To develop in-depth study and research skills, you will undertake a literature review and present a simulated R&D proposal exercise in an area related to your main project area. This will help develop your ability to independently shape and evidence a rigorous research and development plan.  You will also learn how to present a technology business case (or grant proposal) addressed to different stakeholders and audiences, and how to assess human and business perspectives.

c. The individual research project will allow to fully explore AI / ML approaches in the area that you study in-depth, with scope to learn through several cycles of development and evaluation.  Supervision and assessment will be based on progress milestones such as a poster presentation, the MRes thesis and a viva. The work on the research project will stretch over multiple terms.

You will also be required to complete the Imperial College programme of professional skills development courses delivered by the Graduate School, and would attend seminars and journal clubs throughout the year.