Eligibility: all students
Bursary: £16,540 per year
Fees: Tuition fees will be paid by the university
Deadline for applying: May 22 2022
The Faculty of Technology Design and Environment at Oxford Brookes University is pleased to offer a three-year full-time PhD studentship to students commencing September 2022, funded by the EU Horizon 2020 project “Epistemic AI”.
The successful candidate will join the Visual Artificial Intelligence Laboratory under the supervision of Professor Fabio Cuzzolin. This is a fully-funded PhD studentship with annual bursary of £16,540.
The Visual Artificial Intelligence Laboratory is a fast-growing research unit currently running on a budget of £3.2 million from nine live projects funded by the EU (2), Innovate UK (2), the Leverhulme Trust and others. Our research interests span artificial intelligence, uncertainty theory, machine learning, computer vision, autonomous driving, surgical and mobile robotics, AI for healthcare. The Lab is currently pioneering frontier topics in AI such as machine theory of mind, self-supervised learning, continual learning and future event prediction.
The PhD students will join the Lab’s work towards a new Horizon 2020 FET (Future Emerging Technologies) project “Epistemic AI” coordinated by Prof Cuzzolin and whose other partners are TU Delft (Netherlands) and KU Leuven (Belgium). The project started in March 2021 and will end in February 2025.
The project’s overarching objective is to develop a new paradigm for a next-generation artificial intelligence providing worst-case guarantees on its predictions thanks to a proper modelling of real-world uncertainties. The project re-imagines AI from the foundations, with the aim of providing a proper treatment of the ‘epistemic’ uncertainty stemming from a machine’s forcibly partial knowledge of the world by means of advanced uncertainty theory. All new algorithms and learning paradigms are to be tested in the context of autonomous driving.
Candidates should have a strong mathematical background, specifically in optimisation, probability and statistics, and a good first degree in Machine Learning, Artificial Intelligence or related fields. Applicants are also expected to have Research experience in Machine Learning or Artificial Intelligence, and good coding skills in Python and/or C++. Knowledge of uncertainty theory, including belief functions, random sets or imprecise probabilities is desirable, as is experience of coding in Torch, PyTorch, Tensorflow or Caffe, and experience of work in autonomous driving.