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Computer Science, Mathematics, or related areas. • Strong background in at least one of the following: formal methods, SMT solving, abstract interpretation, or model checking. • Experience with verification tools
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, public confidence, and safe deployment on public roads. There is a pressing need for interpretable, verifiable methods that bridge the gap between complex autonomous behaviour and established safety and
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substantial background in fluid mechanics. Essential skills: Strong knowledge of numerical methods Ability to work effectively in a team Desirable skills / experience: Experience of applying CFD to a complex
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NASA, Airbus, and Rolls-Royce – to develop cutting-edge, data-enhanced numerical methods that will transform how future aircraft are conceived and certified. You will work within the PinhoLab (led by
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: Legal Deposit Libraries, Rejected Texts, and New Methods for Negative Bibliography’ (PromPrint). Hosted by the Sussex Digital Humanities Lab (SHL Digital) and led by Principal Investigator Dr Hannah
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Informal enquiries: Elizabeth Hempstead (podiuminstitute@admin.ox.ac.uk ). Formal applications must meet graduate admissions criteria (details are available on the course page of the University website ) and
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cutting-edge experiments, with the goal of improving and challenging current electronic structure theory and develop new types of (ultrafast) imaging methods. The new tools developed will eventually
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develop methodologies (such as acoustic emission method) detecting early signs of damage, leaks, or degradation before they become critical. We will also leverage the latest developments in machine learning
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-informed learning) with hard physical constraints (Navier–Stokes in spectral space) we will develop methods to super-augment experimental data via data assimilation and turn sparse wind-tunnel measurements
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on probability, property, geometric or printability criteria. Doing so, overcomes a key limitation of deterministic ML-based methods, such as tandem-NNs, which can only produce a single solution per target. Unlike