Information for prospective applicants

Reach out if you would like to discuss projects and opportunities. For the projects and positions below, all scientific backgrounds will be considered. Candidates from minorities and under-represented backgrounds are particularly encouraged to apply.

Note that most of the projects I offer involve analysis and modeling of astronomical data, with a focus on either cosmology or Galactic astrophysics. Therefore, some experience in scientific programming and data analysis is very strongly recommended (for example in python, which is currently the language of choice in astrophysics). No specific experience is required in astronomy, cosmology, or theoretical physics, or even statistics or machine learning. But some experience or interest in at least some of those fields is strongly recommended.

Please also carefully read this page on what to expect when working with me.

PhD students

Do not be intimidated to reach out to discuss projects and opportunities. You don’t need to have a 100% specific idea of what you would like to work on. My colleagues and myself will do our best to guide you through administrative procedures. However, for more efficient discussions please nevertheless do some research on the PhD admission process in the Astrophysics group, as well as possible funding sources (STFC, President’s scholarship, etc). I am not describing specific PhD projects here, but it will most likely be in observational cosmology, and involve analysis of real data with sophisticated statistical techniques, and some theoretical modeling. I do not have funding for new PhD students at the moment, but if you are eligible for external funding I would be open to discussing it. I encourage you to contact all the potential supervisors working on topics that interest you.

MSc/MSci students

Every year I offer projects that last between 3 and 9 months, depending on the degree, and can be carried out alone or in pairs (but you will be responsible for splitting the work). All degrees are welcome. Typically, I expect the projects to attract data-minded students in the following MSc/MSci programs: Physics, Data Science, Computer Science, Artificial Intelligence, Electrical Engineering, etc. For the requirements, see the message on top of this page.

Postdocs

Our group can support your application for externally-funded fellowship with us. Examples include: Imperial JRF, Royal Society URF, STFC JRF, etc. Simply contact any of the staff members (including myself).

Undergraduate summer projects (last update Jan 2025):

The list below describes topics of possible projects for undergraduate research. They are directly connected with my research. If you are interested in a topic, please contact me.

Immersive visualizations of asteroids and supernovae detected by the Rubin Observatory

This project aims to develop immersive visualizations for the Data Observatory of Imperial’s Data Science Institute: https://www.imperial.ac.uk/data-science/facilities/data-observatory. It will focus on supernovae and solar system objects (e.g. asteroids) detected by the Rubin Observatory, a new telescope which will start operating in 2025 for ten years and revolutionise astronomy. This will entail researching what simulations are available, and how to best display them in the Data Observatory. Examples of such data can be found at https://www.youtube.com/watch?v=pzsVm1cruu0 and https://www.youtube.com/watch?v=Ch18t9cz-JU Requirements: good operational knowledge of python (e.g. numpy, pandas, astropy, matplotlib), and some background in observational astronomy is desirable. Modeling synthetic galaxy injections in the Dark Energy Survey images This project is about analysing the public catalogs of synthetic source injections performed in the Dark Energy Survey (DES) data. These injections, artificial signals of known properties (i.e., stars or galaxies) added to the real DES images, are crucial for characterizing the survey’s performance and understanding potential systematic biases. This project will involve analysis these injection catalogs to investigate the recovery rate of galaxies in different environments (i.e. around bright stars, or in deep vs shallow areas of the survey) and to characterize systematic biases affecting downstream cosmological inferences. This will entail manipulating very large catalogs of objects, and developing methods for modeling the mapping between the input (true) and output (measured) properties of the galaxies injected in the DES images. The exact goals will be set at the start of the project; this is a cutting edge topic with plenty of space for conceptual and methodological innovation, although the size of the catalogs and the low density of objects per unit area create numerous computational challenges. Requirements: some background in observational astronomy and cosmology, good operational knowledge of python (e.g. numpy, pandas, astropy, matplotlib). References: https://arxiv.org/pdf/2501.05683 and https://arxiv.org/pdf/2012.12825 Project dates are flexible (July-August-September, possibly leaking into June and October) but should cover 8-12 solid weeks in total.

Lyman Break Galaxies in the Hyper-Supreme Cam survey

This project is about the populations of Lyman-break galaxies detected in the Hyper-Supreme Cam (HSC) survey. The aim is to investigate the content of the publicly released catalogs and to reproduce some of results published in the official Goldrush analyses. The goal will be to characterize the color and redshift distributions of these galaxy populations by exploring different methods 1) cross-matching with other data sets (e.g. DESI, JWST), 2) re-processing the deep fields (eg. ‘repeat’ catalogs in the COSMOS field, or degrading the noise manually), 3) processing predictions of galaxy population models. This will entail manipulating very large catalogs of objects, image, and synthetic models of galaxy photometry. The exact goals will be set at the start of the project, since it is embedded in broader cutting-edge research on Lyman Break galaxies in preparation for the improved data from the Rubin Observatory later this year. Requirements: some background in observational astronomy and cosmology, good operational knowledge of python (e.g. numpy, pandas, astropy, matplotlib). References: https://arxiv.org/abs/1704.06535, https://arxiv.org/abs/2108.01090 Project dates are flexible (July-August-September, possibly leaking into June and October) but should cover 8-12 solid weeks in total.

New neural emulators for galaxy photometric data

This project aims at preparing machine learning emulators for galaxy synthetic photometry, following the method of https://arxiv.org/abs/1911.11778. These emulators accelerate calculations of model predictions for the photometry of galaxies, as observed in modern surveys. This is harnessed in order to constrain galaxy populations, galaxy evolution and cosmology, as in https://arxiv.org/abs/2402.00935, https://arxiv.org/abs/2402.00935, https://arxiv.org/abs/2207.07673. This project aims to apply the software infrastructure developed in these projects to new band passes, corresponding to more recent observatories or experiments, in particular JWST, Euclid, Rubin LSST. Once the models are trained, their outputs will be thoroughly validated. This will require improving existing data analysis and visualization scripts. The results will be discussed with the rest of the team, which in turn may lead to training new models with different neural network architectures if needed. Requirements: good operational knowledge of python (e.g. numpy, pandas, astropy, matplotlib). Some background in observational astronomy and machine learning would be appreciated but are not required, since they will be picked up during the project. Project dates are flexible (July-August-September, possibly leaking into June and October) but should cover 8-12 solid weeks in total.