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or more of the following areas: theory for spatial discretisations of PDEs, analysis and design of domain decomposition methods, numerical analysis for stochastic PDEs, numerical analysis for multiphysics
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For the doctoral programme in question, the following are considered as other qualifications: Practical experience with modern machine-learning methods applied to scientific problems. Practical experience with
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at the intersection of numerical analysis, uncertainty quantification, and scientific machine learning. The research will primarily focus on probabilistic methods for data-driven model reduction, with
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Department of Computer Science. The Graphics Group has a long history of internationally recognized research in the areas of Real-Time Rendering, Graphics Hardware and Ray Tracing. The Group has collaborations
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are united in our efforts to understand, explain and improve our world and the human condition. Description of the workplace There is a growing research group in foundations of computer science at Lund
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Doctoral student in development of nanowire devices for photonic neuromorphic computing (PA2026/472)
and analytically, to solve problems independently using the right methods, and to develop an awareness of research ethics. In addition, you will have the opportunity to work on projects, to develop your
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links between dynamics, catalysis and function in protein tyrosine phosphatases, using the tools of computational biophysics. Our research group is highly interdisciplinary, using everything from quantum
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are united in our efforts to understand, explain and improve our world and the human condition. Description of the workplace As a doctoral student you will be employed at the Department of Computer Science in
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tomography, computational resources, and laboratory facilities for experimental mechanics? The Division of Solid Mechanics conducts research within constitutive modelling, nonlinear numerical methods
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: Large‑scale optimization and machine learning: Stochastic and/or (non‑)convex optimization methods, first‑order methods, variance reduction, distributed and parallel optimization, federated learning