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which you will be encouraged and supported to use your initiative and creativity to identify areas for research, develop research methods and extend your research portfolio. In particular, focus
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or a numerate discipline OR equivalent experience. Broad knowledge of probabilistic models, Bayesian inference and machine learning methods. Good knowledge of R, Python or both (links to project source
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statistical methods. We are committed to supporting and developing our team members, through attendance at conferences and access to training in research methods, and will support you to further develop your
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quantitative, qualitative and statistical methods. We are committed to supporting and developing our team members, through attendance at conferences and access to training in research methods, and will support
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(CFD) methods. The role focuses on transitioning legacy CFD solvers to modern, Python-based AI frameworks (e.g., PyTorch, JAX), targeting high-efficiency, scalable simulations of water-air systems with
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. You will have a PhD in Mathematics, Astrophysics, or a closely allied discipline, with a strong background in numerical methods. Ideally you would have expertise in one of the above options, or some
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mathematical thinking, and their role in the development of numerical competence. The postdoc will investigate the role of cognitive heuristics and visual memory in numerical judgments, with a focus on
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approaches. We are now recruiting 3 postdoctoral researchers (https://postdoc.kaust.edu.sa/Pages/Home.aspx ) with a strong computational and/or theory background to develop novel computational methods. We
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, we expect machine learning to be employed to improve accuracy and efficiency of numerical methods, combining advanced technology with scientific research. About the Department of Mathematics at UiB
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, United Kingdom of Great Britain and Northern Ireland [map ] Subject Area: ERC Synergy project Phase-space-inspired Numerical Methods for High Frequency Wave Scattering (PSINumScat) Appl Deadline: 2025/01