329 postdoc-in-thermal-network-of-the-physical-building PhD scholarships in United Kingdom
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51 Faculty of Physics Startdate: 01.09.2025 | Working hours: 30 | Collective bargaining agreement: §48 VwGr. B1 Grundstufe (praedoc) Limited until: 31.08.2029 Reference Reference no.: 4391 We are a
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of the clean energy revolution? This PhD project gives you the chance to design and build the novel materials that could make hydrogen-powered aircraft, electric cars and renewable energy systems safer
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51 Faculty of Physics Startdate: 01.10.2025 | Working hours: 30 | Collective bargaining agreement: §48 VwGr. B1 Grundstufe (praedoc) Limited until: 30.09.2029 Reference no.: 4388 We are a team of
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, energy, and automotive. Among its variants, wire-arc directed energy deposition (WA-DED) is emerging as an increasingly compelling approach for building large-scale structural components. This process
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behaviour through these models using uncertainty quantification/machine-learning (UQ/ML) algorithms To optimise the manufacturing process with the help of the simulation tool To support in the development and
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Thermoacoustics of net-zero swirl stabilised premixed hydrogen combustors Award Summary 100% fees covered for Home students, and a minimum tax-free annual living allowance of £20,780 (2025/26 UKRI
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Applications are invited for a Doctoral Candidate in SHIELD (Strategies for Healing Implant-Associated Infections and Enhancing Longevity in Devices) Doctoral Network. The candidate would be working
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Are you fascinated by working at the intersection of physics and synthetic biology? This PhD project offers a unique opportunity to develop autonomous microswimmers, which are bioinspired structures
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for cardiovascular applications. You will build on our group’s expertise on Physics-Informed Machine Learning (PIML), a powerful approach that combines data-driven AI with the rigour of physical and physiological
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for cardiovascular applications. You will build on our group’s expertise on Physics-Informed Machine Learning (PIML), a powerful approach that combines data-driven AI with the rigour of physical and physiological