Important: Students should not restrict their search for a supervisor to those listed below. Use other sources of information on research groups to find out about possible supervisors. Most UROP research experiences are obtained with staff who do not advertise their availability. However, please also take note of the list of non-participating staff.

UROP Opportunities in the Faculty of Natural Sciences
UROP Opportunities in the Faculty of Natural Sciences
Title of UROP Opportunity (Research Experience) & DetailsExperience required (if any)Contact Details and any further Information

Design of a Novel Neutrino Detector: Neutrino physicists are already measuring physics beyond the Standard Model, whilst the LHC searches for supersymmetry and extra dimensions.

The discovery of neutrino mass and flavour oscillation is the first confirmed observation of physics beyond the Standard Model [1,2]. The next generation of experiments will perform highly sensitive searches for violation of charge-parity (CP) symmetry with neutrinos [3,4]. CP symmetry means that the laws of nature s hould be the same for antimatter seen through a mirror as they are for normal matter. This symmetry is known to be violated by quarks, but at a very small level. If neutrinos violate CP symmetry at a large level, this could explain why the universe is made of matter and not antimatter!

One of the requirements for the success of future experiments is improved understanding of the interactions of neutrinos and antineutrinos with nuclei [5]. This project is to design a new type of detector, comprising a high-pressure gas time-projection-chamber (HPTPC) [6], to make the necessary measurements.

Imperial College is involved in the proposed Hyper-Kamiokande neutrino experiment in Japan [4], which will be the most precise accelerator neutrino oscillation experiment with world-leading sensitivity to νe appearance and νμ disappearance. We are specifically involved in the efforts to design a new near detector capable of measuring neutrino-nucleus interactions with 1% systematic uncertainty.

This project will involve computational work to develop a Monte Carlo simulation of an HPTPC detector, with the goal of optimising the design for neutrino oscillation measurements. The student will learn to use the ROOT and GEANT4 software packages (both use C++) which are standard tools for high energy physics. The end goal of the project is a conceptual detector design suitable for submission to a national lab or funding body.

Contact: Dr Morgan Wascko, Dept of Physics, Faculty of Natural Sciences, Blackett Laboratory, Room 525, South Kensington Campus. Tel: 0207 594 1607. Email: m.wascko@imperial.ac.uk

Simulation, theory and bioinformatics for malaria vector control: Our lab aims to study population demography, migration and selection in Anopheles mosquitoes, an important vector of malaria. We do this using theory, modelling and simulation techniques, as well as genomic data to help us predict how genetic methods of vector control would work in nature. We have the projects listed below that would suit candidates with strong computing skills.

Project 1: Simulations of spatial soft sweeps for joint estimation of effective population size and migration Building on a new method to estimate the contemporary effective size of a population from “soft selective sweeps”, where an advantageous mutation arises multiply and independently (Khatri & Burt, MBE 2019) —the project will develop and use 2D spatial simulations to explore how soft sweeps can be used to jointly estimate effective population size (demography) and migration rates from the Anopheles 1000 genome data. This will use of data science methods, such as Approximate Bayesian Computing (ABC), to do the joint estimation.

Project 2: How demographic histories had shaped the genomic information. Using existing genome simulators of evolutionary processes to investigate how past demographic histories have left an imprint on the genome.



 

Skills and experience required: The student(s) must have sound computing knowledge with good programming skills. An interest in biology and bioinformatics would be an advantage.

Some projects require specific skills. Project 1: Physics/maths background or a biology background with an interest and experience in mathematical modelling would be advantageous. Current code for project 1 is written in Julia, so it’s knowledge would be a plus. Project 2: Someone with hands-on experience with R programming (e.g. loading datasets into R, making plots, writing R functions, for/while loops etc). Ideally the candidate is also knowledgeable in one of the following areas: 1) C programming, 2) R Shiny, development of R packages, 3) monte-carlo simulation and statistical inference. Guidance will be provided.

 

Preferred dates for the UROP: All year — flexible start date. Project 1 is envisioned to be long term. As projects are computational in nature, we expect given covid restrictions they will be remotely supervised.

Contact details: Dr Bhavin S. Khatri, Dept of Life Sciences, Faculty of Natural Sciences, Silwood Park Campus, Kennedy Building 2.9.Email: bkhatri@imperial.ac.uk; Tel: +44 (0) 207 594 2379 (ext 42379).

Bursary: assistance in applying for a third party bursary will be provided if the UROP takes place during the summer vacation.

Additional info: Students who do not meet all the requirements are also encouraged to apply. Some training will be given, but the student should be prepared to work independently most of the time.

 

NEW (10 June 2021)

Machine learning for predicting Wildfire duration and burned areas: Wildfire forecasting has received increasing attention in fire safety science world-widely. Firefighting resources allocation or evacuation of at-risk areas has much to benefit from numerical models which predicts the spread of the fire in space and time. The UROP participant will contribute to the project of applying machine/deep learning algorithms (e.g., random forest, CNN) to predict wildfire durations/burned areas based on satellite images and local environmental features. This project is in in the context of a collaboration between the Leverhulme Centre for Wildfires, Environment and Society and the Data Science Institute (DSI) at Imperial College London. The participant will be co-supervised by Dr.Sibo Cheng and Dr. Rossella Arcucci.

Skills and experience required: Python programming (experience with machine learning packages (e.g. sk-learn, Keras) is a plus), notions about machine learning algorithms.

 

This UROP is offered on a remote basis, with the preferred length being 8 weeks during the summer vacation. If on-campus attendance is possible this will be considered dependent on the College rules in force at the time, but it will not be essential.

A bursary is available to the successful applicant. The value of the bursary is £325 per week. Further details from the contact below.


Applications are especially welcomed by Black students, as well as from individuals who are members of current and historically underrepresented groups.

Contact details: Dr Sibo Cheng, Leverhulme Centre for Wildfires, Environment and Society, Department of Life Sciences, Imperial College London, South Kensington, London,SW7 2AZ. Email: sibo.cheng@imperial.ac.uk. Tel: +447485578412

NEW (10 June 2021)

Fire related vegetation properties in Amazon: Severe fire frequency affects vegetation biodiversity and structural properties. Amazon store large amount of carbon in vegetation and soils. Recent on-the-ground sample plots suggest high vulnerability of different forest types to fire in the southern Amazon-Cerrado transition including seasonal evergreen forest of Mato Grosso (Prestes et al., 2020) and tropical savanna of Columbia (Armenteras et al., 2021). Sensitivity of multispectral Landsat derived vegetation indices (VIs) to burned areas detection, fire severity and vegetation structural properties are extensively reported for tropics, temperate and boreal forest, however the potential of those VIs to fire related forest biophysical properties for tropical evergreen and savanna ecosystem are limitedly studied.

This project will investigate the potential of mapping fire related species diversity and structural properties using Landsat derived ten VIs across 5 sites of Amazon (2 sites in Mato Grosso, Pucallpa, Bojonawi Reserve, Columbian Upland). These sites have different fire regimes based on different vegetation types (seasonal forest, gallery forest, savanna woodland) with different fire frequency in different years. The output will be useful in understanding how wildfires affect vegetation properties at landscape level, developing fire regime definition for the tropics in the LCWES and present results in RSPSoc 2021 Conference.

Skills and experience required: Remote Sensing, Ecology, Analysis skills. Initiative as part of a team

 

The participant will be co-supervised by Dr Ramesh Ningthoujam and Prof. Colin Prentice

This UROP is offered on a remote basis, with the preferred length being 8 weeks during the summer vacation. If on-campus attendance is possible this will be considered dependent on the College rules in force at the time, but it will not be essential.

A bursary is available to the successful applicant. The value of the bursary is £325 per week. Further details from the contact below.


Applications are especially welcomed by Black students, as well as from individuals who are members of current and historically underrepresented groups.

Contact details: Dr Ramesh Ningthoujam, Leverhulme Centre for Wildfires, Environment and Society, Department of Life Sciences South Kensington, London, SW7 2AZ. Email: rningtho@ic.ac.uk

Further readings:

  • Armenteras, D., Meza, M.C., González, T.M., Oliveras, I., Balch, J.K. and Retana, J. (2021). Fire threatens the diversity and structure of tropical gallery forests. Ecosphere, 12(1). e03347.
  • Prestes N.C.C.dos S., et al., including Feldpausch T.R., (2020). Fire Effects on Understory Forest Regeneration in Southern Amazonia, Frontiers in Forests and Global Change, 3, 1-10.
UROP Opportunities in the Faculty of Natural Sciences