Research Opportunities

Postdoctoral Researchers

I do not have vacancies for postdoctoral researchers at present but they will appear here when available.

Postdoctoral Fellowships

I very much encourage young researchers to apply for postdoctoral fellowships. I am happy to support and assist strong candidates that would like to apply for fellowships with MSSL as the host institution.

If you are interested in discussing this further then please email me, including '[Fellowship enquiry]' in the subject of your email. I receive many enquires and so will only reply if your expertise are well matched to my research interests and there is a high chance of submitting a successful application.

More information on various fellowships can he found here:

PhD Students

The PhD projects that I offer are typically multi-disciplinary and include a combination of cosmology, statistics, and informatics (e.g. machine learning, signal processing, harmonic analysis, etc.). A relatively strong mathematical background is usually required for these types of projects. Strong programming skills are also an advantage.

If you are interested in discussing PhD projects further then please email me, including '[PhD enquiry]' in the subject of your email, and attach a CV. I receive many enquires and so will only reply if your expertise are well matched to my research interests and there is a high chance of submitting a successful application.

Further information on how to submit an official application to MSSL can be found here.

Further information on how to submit an official application via the UCL CDT in Data Intensive Science can be found here.

Brief overviews of current projects on offer are given below

PhD project: AI-based statistical inference for cosmology

The current evolution of our Universe is dominated by the influence of dark energy and dark matter, which constitute 95% of its content. However, an understanding of the fundamental physics underlying the dark Universe remains critically lacking. Forthcoming experiments have the potential to revolutionalise our understanding of the dark Universe. Both the ESA Euclid satellite and the Rubin LSST Observatory will come online imminently, with Euclid scheduled for launch in 2022 and the Rubin LSST Observatory having just achieved first light. Sensitive astro-statistics techniques are required to extract cosmological information from weak observational signatures of dark energy and dark matter.

The bedrock of current cosmological analyses is statistical inference, in particular Bayesian approaches. While such approaches provide a complete statistical interpretation of observations, which is critical for robust and principled scientific studies, they are typically slow, in many cases prohibitively so. Alternative analyses are often based on sparse regularisation approaches (e.g. wavelets, sparsity), themselves based on optimisation techniques. While such approaches are typically fast, they lack the complete statistical interpretation of Bayesian inference. More recently, artificial intelligence (AI) techniques have undergone a revolution and are finding widespread application in cosmology. While such approaches are typically fast once trained and can be highly informative, they are often a black-box, lacking interpretability, and typically do not provide statistical information. At present these three analysis methodologies – Bayesian inference, sparse approaches, and AI approaches – are largely disjoint.

In the proposed project we will develop AI-based statical inference techniques, while also integrating sparse approaches where appropriate, to deliver analysis techniques that yield the best of all three methodologies. In particular, we will develop AI emulation approaches to forward model physical processes efficiently and AI-based likelihood-free inference techniques to leverage the speed and flexibility of AI approaches, combined with the interpretability and statistical information provided by Bayesian inference. Once developed, we will apply these techniques to develop a deep understanding of dark matter and dark energy from observations from the Euclid satellite and the Rubin LSST Observatory, which will become available during the project.

The student should have a strong mathematical background and be proficient in coding, particularly in Python. The student will gain extensive expertise during the project in all three typical areas of data science, namely Bayesian inference, sparsity, and AI, and their intersection, which will prepare the student well for a future career either in industry or academia.

PhD project: Geometric deep learning on the celestial sphere for cosmology and beyond

Deep learning has been remarkably successful in the interpretation of standard (Euclidean) data, such as 1D time series data, 2D image data, and 3D video or volumetric data, now exceeding human accuracy in many cases. However, standard deep learning techniques are not effective when applied to data defined on other domains, such as spherical 360° data. For example, if a spherical image is unrolled to yield a planar (Euclidean) image, objects in the resulting planar image are highly distorted, especially if they lie near the edges of the unrolled planar image. Standard deep learning techniques fail catastrophically due to these and other limitations. Essentially they do not know that the data lives natively on the sphere.

In cosmology, wide field observations are made on the celestial sphere giving rise to spherical 360° data, such as observations of the cosmic microwave background (CMB) relic radiation from the Big Bang and observations of cosmic shear of galaxies, which can be used to better understand the nature of dark matter and dark energy. The current evolution of our Universe is dominated by the influence of dark energy and dark matter, which constitute 95% of its content. However, an understanding of the fundamental physics underlying the dark Universe remains critically lacking. Forthcoming experiments have the potential to revolutionalise our understanding of the dark Universe. Both the ESA Euclid satellite and the Rubin LSST Observatory will come online imminently, with Euclid scheduled for launch in 2022 and the Rubin LSST Observatory having just achieved first light. These experiements will capture wide-field data for which the underlying spherical geometry must be accurately modelled. Thus, spherical deep learning techniques will be essential for next-generation deep learning analysis of the data.

McEwen and colleagues have recently developed efficient generalised spherical convolutional neutral networks (Cobb et al. 2020) that have shown exceptional performance. These techniques are now starting to be applied in virtual reality and in medical imaging.

The focus of the current project is two-fold. First, spherical deep learning techniques will be applied to the analysis of cosmological data of the CMB and of cosmic shear, in order to better understand the nature of dark matter and dark energy and recover maps of the dark matter distribution. Second, further foundations of spherical deep learning will be developed, with new types of spherical deep learning layers and architectures. Moreover, additional applications beyond cosmology, such as for diffusion MRI in medical imaging, may also be considered. The precise focus between these different areas will depend on the interests and expertise of the student.

The student should have a strong mathematical background and be proficient in coding, particularly in Python. The student will gain extensive expertise during the project in deep learning, going far beyond the straightforward application of existing deep learning techniques, instead focusing on novel foundational deep learning approaches and their application to novel problems in cosmology and beyond. The expertise gained in foundational deep learning will prepare the student well for a future career either in industry or academia.

Masters Students

I am not offering any additional Masters projects at present but when I am they will appear here.

Internship Students

I am not offering any specific internship projects at present. However, if you are interested in discussing internship possibilities further then please email me, including '[Internship enquiry]' in the subject of your email, and attach a CV. I receive many enquires and so will only reply if your expertise are well matched to my research interests and there is a high chance of a placement.