108 parallel-computing-numerical-methods positions at Loughborough University in United Kingdom
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Computer Vision and Machine Learning Specialist KTP Associate £34,000 to £38,000 per annum plus training budget of up to £4,500. Department of Computer Science TrainFX Computer Vision and Machine
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for informal enquiries: Hatty Bates, Performance Programme Manager, H.Bates@lboro.ac.uk Closing date: 25th April 2025 Interview date: 8th May 2025 Loughborough University is committed to achieving equity and
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in analytical and/or numerical methods for interacting quantum systems. Familiarity with constrained dynamics, kinetic constraints, glassy systems, or random matrix techniques would be an advantage but
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Administration team. We are seeking applicants with previous experience of working in a financial role who have a good level of numerical accuracy and are committed to providing an outstanding customer service as
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, multi-phase flows, reacting flows, advanced experimental numerical techniques, high fidelity computational fluid dynamics, and the use of AI. The successful candidate will be expected to collaborate with
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multidisciplinary team at Loughborough University’s Wolfson School. Our PERAMEDIC project brings together experts in material formulation, deposition methods, hardware development and clinical translation from across
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. A particular focus will be on the soil micro-structure evolution and how this links to macro-scale behaviour, using advanced laboratory tests, imaging techniques, and computer simulations. The
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of porous materials and lithography, aiming to develop novel methods for patterning porous structures. Porous materials, such as hydrogen-bonded organic frameworks (HOFs), are characterized by their high
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until April 2026 and develops methods for reinforcement learning of motion controllers for autonomous vehicles. The Research Associate will implement reinforcement learning and model predictive control
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of sources and field trails. In addition, the more specific duties in this project require you to expand and augment existing graph-based SLAM methods to enable multi-session SLAM to be preformed in