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. Programming skills are also an advantage. Some example projects are listed below, however the scope of projects can be modified to meet the interests of potential students.

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.

Some examples of illustrative projects follow.

High-dimensional uncertainty quantification for radio interferometric imaging

Radio interferometric telescopes allow astronomers to make radio observations of the sky at otherwise inaccessible angular resolution and sensitivity. We are about to enter a new era of radio astronomy, with new radio interferometers under construction and design. One notable example is the Square Kilometre Array (SKA), whose science goals range from cosmology and astrobiology, to strong field gravity. The SKA will usher in a new big-data era of radio interferometry. Novel imaging techniques will be required to ensure that new radio telescopes can handle this big-data regime and meet their scientific goals.

Sparse reconstruction methods, motivated by the theory of compressive sensing, have recently shown a great deal of promise for radio interferometric imaging. Furthermore, algorithms have been developed to efficiently scale these techniques to the emerging big-data era of radio interferometry. However, until our recent work, no radio interferometric imaging techniques used in practice, including sparse approaches, can quantify the uncertainties associated with reconstructed images, i.e. no methods can recover error bars on recovered images. Recently, we merged Bayesian and sparse paradigms to yield the advantages of each appraoch, developing techniques to quantify uncertainties and scale to big-data.

Uncertainty quantification in high-dimensions (like radio interferometric imaging) is a topical problem in data science at present. The goal of this project is to further develop uncertainty quantification techniques that scale to big-data and to apply these techniques to perform a variety of scientific studies. The main focus will be scientific studies for radio astronomy, although other applications can be considered (e.g. medical imaging) depending on the interests of the student.

Recommended reading

Deep learning for radio interferometric imaging

Radio interferometric telescopes allow astronomers to make radio observations of the sky at otherwise inaccessible angular resolution and sensitivity. We are about to enter a new era of radio astronomy, with new radio interferometers under construction and design. One notable example is the Square Kilometre Array (SKA), whose science goals range from cosmology and astrobiology, to strong field gravity. The SKA will usher in a new big-data era of radio interferometry. Novel imaging techniques will be required to ensure that new radio telescopes can handle this big-data regime and meet their scientific goals.

Recently, deep learning techniques have emerged as a powerful alternative approach for solving inverse imaging problems like radio interferometric imaging. The goal of this project is to develop and apply novel deep learning techniques for radio interferometric imaging, which presents unique challenges beyond the standard deep imaging scenarios. These techniques will be essential not only to recover high-fidelity images from the raw data acquired by radio telescopes but also to handle the emerging big-data era of radio interferometry.

Recommended reading

Machine learning and informatics techniques for cosmology

Symmetry breaking phase transitions in the early Universe may have lead to the creation of topological defects. Cosmic strings are one particular type of defect, where axial or cylindrical symmetry is broken, leading to line-like discontinuities in the fabric of the Universe. Although we have not yet observed cosmic strings, we have observed string-like topological defects in other media, such as liquid crystals (see image). Note that cosmic strings are distinct to the fundamental superstrings of string theory. However recent developments in string theory suggest the existence of macroscopic superstrings, which could play a similar role to cosmic strings. Spacetime about a cosmic string is conical. Consequently, strings moving transverse to the line of sight induce line-like discontinuities in the cosmic microwave background (CMB), the relic radiation of the Big Bang. The detection of cosmic strings from CMB observations would open a new window into the physics of the early Universe.

The goal of this project is to develop and apply machine learning methods for cosmology. Depending on the interests of the student, one particular application is to search for evidence of cosmic strings from observations of the CMB. In the absence of a detection, the allowable string tension (energy level) will be constrained. Novel techniques will be developed by combining ideas from Bayesian inference, machine learning and compressive sensing.

Recommended reading

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.