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are seeking a Research Fellow to contribute to a project focused on Numerical Methods for McKean-Vlasov PDEs. Key Responsibilities: Develop Numerical Methods for McKean-Vlasov PDEs/Master equations Job
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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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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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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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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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AUSTRALIAN NATIONAL UNIVERSITY (ANU) | Canberra, Australian Capital Territory | Australia | 3 days ago
simulations to examine protein structure and function. This includes using advanced sampling methods and free energy calculations. The role involves publishing research in peer-reviewed journals, presenting
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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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Australian National University | Canberra, Australian Capital Territory | Australia | about 21 hours ago
independent research in molecular biology and biophysics, focusing on computational simulations to examine protein structure and function. This includes using advanced sampling methods and free
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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