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Field
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to learn laboratory methods for analysis of relevant BGC parameters. Training: You will be based in the Polar Oceans Team at British Antarctic Survey, a highly active research team focused on both
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Max Planck Institute for Gravitational Physics, Potsdam-Golm | Potsdam, Brandenburg | Germany | about 6 hours ago
, gravitational self-force, black-hole perturbation theory, and effective-one-body theory), numerical-relativity, most notably simulations of compact objects in general relativity and alternatives, interpretation
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for their employability in applications. Additionally, machine learning methods need to be applicable to high-dimensional and to noisy data that are typically encountered in real-world applications. The aim of this project
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in Discontinuous Galerkin Finite Element Methods and the Julia programming language will admitted to the selection. Workplan and the objectives to achieve: This scholarship aims to advance a time
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profile Completed university studies (Master/Diploma) in the field of Physics (Computational-, Plasma Physics, Optics) or related field Mastery and use of the scientific method Experience in numerical
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remaining functional under harsh geothermal conditions. The candidate is expected to work closely with D-TECH and may undertake a secondment period to test and apply the developed methods on industrial-scale
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accuracy requires high spatial and temporal resolution, which is time-prohibitive and therefore impractical for large parts. This project therefore aims to develop numerical methods that enable the efficient
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requirements: • Master degree in theoretical physics, computer science or communication systems • Excellent programming skills • Experience in optimization, numerical methods or learning • Proficiency in spoken
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knowledge of numerical methods - knowledge of free-surface flows, granular flows - proven experience in numerical simulation (finite elements, finite volumes, CFD, LES, etc.) - proficiency in basic scientific
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to the proposed control methods in respect of heat transfer and drag. *Keywords : flow control, heat transfer, wall-bounded flow, thermal boundary layer, numerical simulations, reduced order model, machine learning