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completion of a PhD in geomechanics, energy engineering, geophysics, and/or geodynamics. An established record of scientific achievements in geomechanics and geomechanical numerical modelling, including
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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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. Demonstrate sufficient breadth and depth of specialist knowledge in the discipline and of research methods and techniques to work within established research programmes. Show a proven ability to communicate
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. Demonstrate sufficient breadth and depth of specialist knowledge in the discipline and of research methods and techniques to work within established research programmes. Show a proven ability to communicate
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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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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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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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, 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