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- Application deadline: Midnight, 9 Jan 2022
- Starting date: As soon as possible
- Salary: £35,931-£37,979
- Duration: until February 2023
Applications are invited for the post of Post-Doctoral Research Assistant in
the Computer Science Department at Royal Holloway, University of London.
Successful applicants will be working on the EPSRC-funded "Verification of
Hardware Concurrency via Model Learning" (CLeVer) project (EP/S028641/1), led
by Alexandra Silva (UCL/Cornell) and Matteo Sammartino (Royal Holloway
University of London).
This is a joint research effort involving Royal Holloway University of London,
University College London, and ARM, world-leading designer of multi-core chips.
For an informal discussion about the post, please contact Dr. Matteo Sammartino
on matteo.sammart...@rhul.ac.uk.
# Project Description
Digital devices increasingly rely on multi-threaded computation, with
sophisticated concurrent behaviour becoming prevalent at any scale. As the
complexity of these systems increases, there is a pressing need to automate the
assessment of their correctness, especially with respect to concurrency-related
aspects. Formal verification provides highly effective techniques to assess the
correctness of systems. However, formal models are usually built by humans, and
as such can be error-prone and inaccurate.
The CLeVer project aims to:
- develop a novel verification framework that relies on learning techniques to
automatically build and verify models of concurrency, with a particular focus
on multi-core systems.
- apply the framework to real-world verification tasks, in collaboration with
ARM.
# The ideal candidate
We are looking for candidates with a PhD in one of the following areas:
model-based testing and verification, formal methods for concurrency, automated
analysis of hardware systems. Experience in multiple areas will be valued.
Candidates ideally should also have strong programming skills.
# Where to apply
https://urldefense.com/v3/__https://jobs.royalholloway.ac.uk/0721-259-R-R__;!!IBzWLUs!GBUI4ogBjKihbUy7Zjvs5uy-jYLfnkDBmHoLxe66Aocp0u-vNUn0dwNvEzw0ElB4e1xgKh3NbHDrlQ$
