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to accelerate solving AC power flow (AC-PF) computations, potentially facilitating real‑time contingency analysis, rapid design‑space exploration, and on‑line operational optimization of power systems
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, train and test novel machine-learning-based solutions on top-tier super-computing hardware Work in an interdisciplinary team of engineers, computer scientists, and life scientists Regularly participate in
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interdisciplinary team of engineers, computer scientists, and life scientists Present your work at international conferences and learn about state-of-the-art methods in machine learning, reinforcement learning and
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Bayesian computational statistics, differentiable programming, and high-performance computing, the project aims to deliver robust, interpretable, and scalable methods for metabolic flux analysis. You will
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, energy systems, or material sciences A Masters degree with a strong academic background in mathematics, computer science, physics, material science, earth science, life science, engineering, or a related
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disciplines (meteorology, environmental science, high performance computing, software development and data science). Your Profile: Mandatory qualifications are: M. Sc. degree in meteorology, physics