Research Associate on Verified Reinforcement Learning in Automotive

<div xmlns="http://www.w3.org/1999/xhtml"> <p></p><p>We have an exciting opportunity within Computer Sciences, the successful candidate will be required to work on a project where you will develop solutions for verification issues for complex cyber-physical systems in the automotive domain, which include black-box learning/adaptive components. You will engage with researchers and colleagues in the areas of Automated Verification and of Machine Learning. The project is bolstered by an industrial partner, a Tier 1 automotive company.</p><br> <p>You will be employed by the University of Oxford, Department of Computer Science as a Research Associate, working with Professor Alessandro Abate (see: <a rel="nofollow" href="https://www.oxcav.org/">www.oxcav.org/</a>). You will be responsible for delivering the project objectives. You will manage your own academic research and administrative activities, and collaborate in the preparation of scientific reports and journal articles and present papers and posters. You will act as a source of information and advice to other members of the group on scientific protocols and experimental techniques. You will represent the research group at external meetings/seminars, either with other members of the group or alone.</p><br> <p>The primary selection criteria are a doctorate in computer science, engineering, mathematics, or related discipline (or close to completion) together with relevant experience in the areas of this project, expertise in computational methods, mathematical modelling, numerical methods, and code development. We hope to fill up the position by March 2020.</p><br> <p>The closing date for applications is 12.00 noon on Wednesday 12 February 2020. Interviews are expected to be held on or around 17 February 2020.</p><br> <p>This is a fixed-term post for up to 1 year</p><br> <p>Our staff and students come from all over the world and we proudly promote a friendly and inclusive culture. Diversity is positively encouraged, through diversity groups and champions, for example <a rel="nofollow" href="http://www.cs.ox.ac.uk/aboutus/women-cs-oxford/index.html">www.cs.ox.ac.uk/aboutus/women-cs-oxford/index.html</a>, as well as a number of family-friendly policies, such as the right to apply for flexible working and support for staff returning from periods of extended absence, for example maternity leave.</p> </div>