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Field
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Performance Computing, computational and cutting-edge data analytics methods, and growth and retention of vital capacity in research software engineering and technical R&D are at the core of this endeavour. We
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interdisciplinary collaborations. Leading-edge platforms including High Performance Computing, computational and cutting-edge data analytics methods, and growth and retention of vital capacity in research software
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advanced many-body methods, high-performance computing, and machine learning approaches. The successful candidate will play a leading role in developing computational methods and high-performance algorithms
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completion) in applied mathematics, computer science, or a closely related field. Strong background in numerical linear algebra, algorithm design, and parallel computing. Proficiency in programming languages
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Inria, the French national research institute for the digital sciences | Pau, Aquitaine | France | 20 days ago
Galerkin methods, which are particularly well-suited for parallel computing, and it typically requires solving large-scale linear systems during simulations. The main objectives of this position are to: (1
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quantitative image analysis, numerical modeling, and explainable AI (XAI) with state-of-the-art biophysical methods. Using techniques such as traction force microscopy, microfluidics, 3D bioprinting, and
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of numerical quantum many-body methods to study model Hamiltonians. Strong background in linear algebra. Preferred Qualifications: Experience with density matrix renormalization group and tensor network
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Helmholtz-Zentrum Dresden-Rossendorf - HZDR - Helmholtz Association | Dresden, Sachsen | Germany | about 9 hours ago
Neural Operators. Your tasks # Design, train and tune surrogate models for electromagnetic field solvers using neural operators # Use symmetries of the modeled plasma physics and numerical methods
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of the research engineer is to set up multi-physics simulations with large supercomputers on the perimeters of the project by developing advanced pre- and post-processing, the necessary models and numerical methods
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of interpretability methods to ensure ML outputs are meaningful in scientific contexts. Preferred: Background in biomedical data, healthcare, or AI for life sciences. Experience with parallel computing. Familiarity