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Applicants are invited for a PhD fellowship/scholarship at Graduate School of Technical Sciences, Aarhus University, Denmark, within the Electrical and Computer Engineering programme. The position
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nitrogen-containing products and assess system performance. You will contribute to the design and testing of the prototype system, analyse experimental and simulation data, and publish the resulting research
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The successful candidate should possess the following: • A PhD in Materials Science, Chemical Engineering, Chemistry, Physics, or related fields • Highly motivated, committed, and capable
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PhD Studentship: West Nilotic Morphosyntax The topic is the comparative morphosyntax of the West Nilotic languages, and forms part of the European Research Council-funded project ‘NILOMORPH
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Our group aims to simulate how stars and planets form and how black holes accrete. I am offering PhD projects in all areas of accretion physics. Possible PhD projects in star and planet formation
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this, the postdoctoral researcher will combine machine learning, molecular dynamics simulations and high performance computing (Isambard AI). Applicants must have a PhD in an appropriate area of computational chemistry or
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with Simulation-based Inference ), funded by the Multidisciplinary Institute for Artificial Intelligence (MIAI) through an AIforScience Research Chair. ADACSI will develop a multi-probe simulation-based
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to simulate the thermo-hydro-mechanical behavior of Singapore soils and investigate the thermomechanical performance of geostructures. Publish high-quality research findings in leading peer-reviewed journals
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larger simulation environment that evaluates the aviation system of the future. The PhD should consider: Research: Characterise how new aircraft types differ in performance from current traffic, and what
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4-year PhD fellowship in the Research Programme - Deep Learning-Accelerated Crystallography Pipeline
We welcome applications from candidates with a broad range of academic backgrounds and experiences for a 4-year PhD project on Mathematical and Machine Learning Aspects in Crystallography at Durham