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Senior Research Associate on FUN2MODEL: From FUNction-based TO MOdel-based automated probabilistic reasoning for DEep Learning

Computer Science

University of Oxford, Oxford, UK


  • Posted on: 16th Sep 2021
  • Salary: Grade 8: Salary £42,149 - £50,296 p.a.
  • REF: 153319
  • Closes: 14th October 2021
  • Contract Type: Fixed-Term/Contract
  • Hours: Full Time

Job Details

Senior Research Associate on FUN2MODEL: From FUNction-based TO MOdel-based automated probabilistic reasoning for DEep Learning

Computer Science – Wolfson Building, Parks Road, Oxford

Grade 8: Salary £42,149 – £50,296 p.a.

Department of Computer Science, Parks Road, Oxford.

Senior Research Associate on FUN2MODEL: From FUNction-based TO MOdel-based automated probabilistic reasoning for DEep Learning

Full Time and Fixed Term until 30th September 2024

Grade 8: Salary £42,149 – £50,296 p.a. (note: Grade 7: £33,309 – £40,927 p.a.)

We are looking for a motivated Senior Research Associate to play a key role in the ERC funded FUN2MODEL project.

You will be a senior member of the collaborative project team, reporting directly to Professor Marta Kwiatkowska, and you will provide leadership for 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 be responsible for carrying out research with an emphasis on probabilistic reasoning and inference, including cognitive aspects and automated decision making. This may involve neuro-symbolic approaches; probabilistic verification/synthesis; causal reasoning; planning and game-theoretic methods; robustness and certification. Suitably qualified candidates will have an opportunity to lead software implementation, liaising with Dave Parker to coordinate PRISM codebase extensions.

You will be expected to regularly write research articles at a national level for peer-reviewed journals, agree clear task objectives, organise and delegate work to other members of the team, and share responsibility for shaping the research group’s plans.

You should hold a PhD in computer science, mathematics or related discipline and have post qualification research experience, possess specialist knowledge and demonstrable experience across some/all 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. Knowledge of neural networks and Bayesian methods is highly desirable.

Whilst the role is a Grade 8 position, we would be willing to consider candidates with potential but less experience who are seeking a development opportunity, for which an initial appointment would be at Grade 7 (£33,309 – £40,927 p.a.) with the responsibilities adjusted accordingly; for Grade 7, you would be expected to hold a doctoral degree (or be close to completion) in Computer Science, mathematics, or related discipline. This would be discussed with applicants at interview/appointment where appropriate.

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 14th October 2021. Interviews are expected to be held in the second half of October 2021.

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.

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14-Oct-2021 12:00

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153319 – Job description and selection criteria


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