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Research Fellow in Autonomous Driving under Uncertainty

Oxford Brookes University - Faculty of Technology, Design & Environment

Location: Oxford
Salary: £35,880 rising to £39,105
Hours: Full Time
Contract Type: Fixed-Term/Contract
Placed On: 23rd December 2024
Closes: 19th January 2025
Job Ref: 174959
The Visual Artificial Intelligence Laboratory (VAIL) at Oxford Brookes University, led by Professor Fabio Cuzzolin, is seeking a Research Fellow in Autonomous Driving under Uncertainty, to be appointed as soon as possible, full-time, for a period of 12 months or until the end of the project (February 2026).
 
The Research Fellow will join the Lab’s efforts in the context of the Horizon 2020 FET-Open project “Epistemic AI” coordinated by Oxford Brookes, with KU Leuven and TU Delft as the other partners.
 
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.
https://www.epistemic-ai.eu/ 
 
The Fellow will assist Prof Cuzzolin with the coordination of the H2020 Epistemic AI project and maintain a smooth communication among the project partners. In particular, they will lead the research work of Work Package 3 (Science-to-technology breakthrough in autonomous driving), by developing new frameworks for object detection and scene understanding under uncertainty, as well as inverse reinforcement learning. They will join another research fellow and co-supervise the two PhD students hired under the project, while helping with the general management of the Visual AI Lab.
 
The Visual Artificial Intelligence Laboratory is a fast-growing research unit, very active in deep learning for action detection, uncertainty in artificial intelligence, machine learning (continual and self-supervised 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, evolving machine intelligence, intrinsically-aligned AI, epistemic foundation models, AI for science, neural operators under uncertainty. Within autonomous driving, we are recognised as the creators of the series of ROAD datasets for situational awareness,
 
https://www.computer.org/csdl/journal/tp/2023/01/09712346/1AZL0P4dL1e 
https://sites.google.com/view/road-plus-plus/dataset 
https://sites.google.com/view/road-r/dataset 
  
The organisation of 5 recent workshops at top conference (ICCV, ECCV, UAI, IROS), and we are working to incubate a spinoff company on robust perception and end-to-end planning for autonomous vehicles.
VAIL is also a core member of the University’s new Institute for AI, Data Analysis and System (AIDAS), also directed by Prof Cuzzolin.
You are encouraged to contact Prof Cuzzolin at fabio.cuzzolin@brookes.ac.uk for more information and informal feedback on your application.
 
The deadline for application is 19 January 2025.
 
As one of the largest employers in Oxford we pride ourselves in the great experience we offer our staff. You’ll be joining a friendly, professional environment where every member of staff is recognised as important to the success of Oxford Brookes University. To find out more about the benefits of working for Oxford Brookes please visit: www.brookes.ac.uk/job-vacancies/working-at-brookes.
 
At Oxford Brookes, inclusivity is one of our five guiding principles: we take positive steps to create an environment in which we celebrate, value and provide equal opportunity to all. We are committed to recruiting from the widest possible pool and welcome applications from all suitably qualified candidates who embrace our values, regardless of their background.
 
As a Disability Confident Employer, we guarantee to interview any disabled applicant who meets the essential selection criteria.
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