Research Associate on FUN2MODEL
Posted: 6th June 2019
Department of Computer Science, Parks Road, Oxford.
Research Associate on FUN2MODEL: From FUNction-based TO MOdel-based automated probabilistic reasoning for DEep Learning
Fixed term for 3 years from 1st October 2019, with the possibility of extension
Grade 7: £32,236 - £39,609 p.a.
The Automated Verification group at the Department of Computer Science has a vacancy for a Research Associate on the ERC funded FUN2MODEL (From FUNction-based TO MOdel-based automated probabilistic reasoning for DEep Learning) project. Reporting to Professor Marta Kwiatkowska, you will conduct original research on this project with an emphasis on developing automated probabilistic verification and synthesis methods for machine learning components. This includes Bayesian interpretation; provable probabilistic robustness guarantees for neural networks; provably correct synthesis for neural networks; complex correctness properties for machine learning decisions; software implementation; and relevant case studies.
You will be responsible for carrying out research as outlined above, collaborating with Professor Kwiatkowska and other members of the team, and assisting with project reporting, as required. You will also have an opportunity to engage in teaching, and to provide guidance to junior members of the research group, including PhD and MSc students.
You should hold a PhD (or be close to completion) in computer science, mathematics, or related discipline and have post-qualification research experience, possess sufficient specialist knowledge of foundations of machine learning and/or statistics, Gaussian processes, and probabilistic programming or Bayesian networks, and have an excellent track record of peer-reviewed publications. Familiarity with verification for neural networks and/or probabilistic verification/synthesis is highly desirable.
The closing date for applications is 12 noon on 8 July 2019. Interviews are expected to be held on 23 or 24 July 2019.
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