The Department of Mathematical Sciences at the University of Bath is inviting applications from excellent candidates for a PhD studentship in the EPSRC Programme Grant EP/V026259/1 on the “Mathematics of Deep Learning” (Maths4DL). Maths4DL is a joint programme run by the University of Bath, the University of Cambridge and University College London.
Start date: 2 October 2023.
This is an exceptional opportunity to conduct ambitious research at the forefront of mathematics, statistics, and machine learning. The Vision and Ambition of Maths4DL is to develop a comprehensive mathematical, statistical and computational framework that addresses i) the foundational challenges on interpretability of the trained Deep Neural Networks (DNN), ii) robustness and generalisation of DNN on test data, iii) statistical confidence in DNN-outputs and iv) computational challenges with large-scale models and high-dimensional training data.
Project Overview:
Inverse problems arise when recovering a quantity of interest from indirect measurements. Several important applications can be modelled as an IP, ranging from biomedical imaging to non-destructive material testing and computer vision. Recently, data-driven approaches (especially, deep learning) have been widely applied to the solution of inverse problems, with very successful results, despite a lack of understanding of their performance guarantees. The goal of the “Leveraging data-driven approaches in inverse problems” project is to explore how to integrate more traditional approaches for the solution of inverse problems within a data-driven framework to develop theoretically motivated neural network architectures, together with their mathematical analysis and implementation.
Project keywords: deep learning, machine learning, inverse problems, imaging, optimisation, regularisation.
Candidate Requirements:
Applicants should hold, or expect to receive, a First Class or good Upper Second Class Honours degree (or the equivalent) in mathematics or a closely-related discipline. A master’s level qualification would also be advantageous.
The ideal candidate will have experience in one or more of the following areas: inverse problems, machine learning, mathematical imaging, variational reconstruction approaches and/or optimisation. Experience in programming is highly desirable (e.g. MATLAB / Python, Tensorflow / PyTorch). The ideal candidate should also have strong communication and organisation skills and be a team player.
Non-UK applicants must meet our English language entry requirement.
Enquiries and Applications:
Informal enquiries are welcomed and should be directed to Dr Tatiana Bubba on email address [Email Address Removed].
Formal applications should be submitted via the University of Bath’s online application form for a PhD in Mathematics.
More information about applying for a PhD at Bath may be found on our website.
Note: Applications may close earlier than the advertised deadline if a suitable candidate is found. We recommend, therefore, that you contact Dr Bubba prior to applying and submit your formal application as early as possible.
Funding Eligibility:
To be eligible for funding, you must qualify as a Home student. The eligibility criteria for Home fee status are detailed and too complex to be summarised here in full; however, as a general guide, the following applicants will normally qualify subject to meeting residency requirements: UK and Irish nationals (living in the UK or EEA/Switzerland), those with Indefinite Leave to Remain and EU nationals with pre-settled or settled status in the UK under the EU Settlement Scheme. This is not intended to be an exhaustive list. Additional information may be found on our fee status guidance webpage, on the GOV.UK website and on the UKCISA website.
Equality, Diversity and Inclusion:
We value a diverse research environment and aim to be an inclusive university, where difference is celebrated and respected. We welcome and encourage applications from under-represented groups.
If you have circumstances that you feel we should be aware of that have affected your educational attainment, then please feel free to tell us about it in your application form. The best way to do this is a short paragraph at the end of your personal statement.
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