PhD Studentship in Epistemic Artificial Intelligence
Closing date: 20 June 2021
The Faculty of Technology, Design and Environment at Oxford Brookes University is pleased to offer 2 three-year full-time PhD studentships to students commencing September 2021, funded by the European Union’s Horizon 2020 research and innovation programme under grant agreement No 964505 “Epistemic AI”. The successful candidates will join the Visual Artificial Intelligence Laboratory under the supervision of Professor Fabio Cuzzolin.
The Visual Artificial Intelligence Laboratory is a fast-growing research unit currently running on a budget of £3 million from eight 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 has duration of 4 years. 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.
We seek a highly competent candidate to submit their thesis within 3 years. 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.
The studentship includes a bursary of £16,540 per year and tuition fees for 3 years.
Call for Events is now open! We're supporting Members and Expert Fellows to lead activities that explore aspects of TIPS in the Digital Economy. We will help to organise the activity with up to £5,000 to cover the associated costs.