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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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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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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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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
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methods to create precisely controlled 3D structures Developing routes the depolymerise and re-print the materials in a closed loop manner Undertaking analysis to understand the green chemistry and
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experience in a range of industrially relevant computational engineering techniques. You will develop expertise in high-order finite element methods, mesh adaptation techniques, advanced parallel programming
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University of Salford, focusing on technology-informed rehabilitation of musculoskeletal injuries in military personnel. Force plates are widely used for multi-joint assessments, with standardized methods
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to join CARE-HSC, specifically working on ‘Workstream 1: Formal (Workforce) Carers’ co-led by Professor Stephani Hatch and Dr Rebecca Rhead. This workstream focusses on a collaboration with NHS peer