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Position: Research Associate on FUN2MODEL: From FUNction-based TO MOdel-based automated probabilistic reasoning for DEep Learning
Institution: University of Oxford
Location: Oxford, Oxfordshire, United Kingdom
Duties: We are looking for two motivated Research Associates to play key roles in the ERC funded FUN2MODEL project. You will be a member of the collaborative project team working at the cutting edge of Computer Science. Reporting directly to Professor Marta Kwiatkowska, you will be contributing to the development of theories, models and algorithms for quantitative/probabilistic verification and synthesis to enable robust AI. Based within an internationally leading research group, you will benefit from working in Oxford University’s acclaimed Computer Science Department, located in the heart of Oxford’s Scientific Keble Triangle
Requirements: You should hold a PhD (or be close to completion) in computer science, mathematics or related discipline, possess sufficient specialist knowledge across some/all areas of: symbolic/neuro-symbolic methods; probabilistic/statistical verification and synthesis; planning and game theory, as well as have proven experience of software development in relevant areas, such as SAT/SMT, statistical inference, constraint solving and optimisation. Familiarity with neural networks and Bayesian methods is desirable
   
Text: University of Oxford UK date and time: 14-January-2022 14:13 Applicant Options New Search New Search Login Login Job Details Job Details Help Help Terms of Use & Privacy Policy Terms of Use & Privacy Policy Core Solutions Image Job Details Research Associate on FUN2MODEL: From FUNction-based TO MOdel-based automated probabilistic reasoning for DEep Learning Computer Science, Wolfson Building, Parks Road, Oxford Grade 7: £33,309 - £40,927 p.a. We are looking for two motivated Research Associates to play key roles in the ERC funded FUN2MODEL project. You will be a member of the collaborative project team working at the cutting edge of Computer Science. Reporting directly to Professor Marta Kwiatkowska, you will be contributing to the development of theories, models and algorithms for quantitative/probabilistic verification and synthesis to enable robust AI. Based within an internationally leading research group, you will benefit from working in Oxford University’s acclaimed Computer Science Department, located in the heart of Oxford’s Scientific Keble Triangle. You will carry out research on probabilistic verification and synthesis to enable robust AI. This may involve neuro-symbolic approaches; probabilistic verification/synthesis; planning and game-theoretic methods; robustness and certification. Suitably qualified candidates will have an opportunity to implement software, liaising with Dave Parker to coordinate PRISM codebase extensions. You will be expected to write research articles for leading conferences and journals, complete clear task objectives, organise your workload, and proactively contribute towards the research group’s objectives. You should hold a PhD (or be close to completion) in computer science, mathematics or related discipline, possess sufficient specialist knowledge across some/all areas of: symbolic/neuro-symbolic methods; probabilistic/statistical verification and synthesis; planning and game theory, as well as have proven experience of software development in relevant areas, such as SAT/SMT, statistical inference, constraint solving and optimisation. Familiarity with neural networks and Bayesian methods is desirable. We would particularly welcome applications from women and black and minority ethnic applicants who are currently under-represented within the Computer Science Department. The closing date for applications is 12 noon on Monday, 7th February 2022. Interviews are expected to be held on the week commencing 14th February 2022. 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 http://www.cs.ox.ac.uk/aboutus/women-cs-oxford/index.html, 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. Contact Person : HR Administrator Vacancy ID : 155447 Contact Phone : Closing Date & Time : 07-Feb-2022 12:00 Contact Email : hr@cs.ox.ac.uk Click on the link(s) below to view documents Filesize 155447 - Job description and selection criteria 169.1 Return to Search Results Apply Now
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