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Field
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teams to integrate AI and machine learning techniques into lattice field theory frameworks. - Engage in large-scale numerical simulations, performance analysis, and optimization using state-of-the-art
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to produce optimal designs. Applicants should have skills in modelling, familiarity with partial differential equations, and be familiar with python. They will have, or be close to completing, a PhD in
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power (CSP) systems optimization, computational heat transfer and radiative transport using sophisticated numerical modeling and machine learning approaches for forward and inverse problems in radiation
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interactions. Orbit dynamics evolve over a longer timescale compared to the rapid dynamics of attitude and GNC systems. Simulating these subsystems together requires sophisticated numerical methods to maintain
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-to-Decadal Variability & Predictability Division, Technical Services and Modeling Systems Division. The selected candidate will have access to state-of-the-art numerical models and high-performance computing
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integration of vehicles into mobility and energy systems. We improve the efficiency, sustainability and economics of electric vehicles by optimizing and accelerating the integration of components up to complex
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areas will be considered when selecting candidates: Machine Learning, Neural Networks, Numerical solutions of Partial Differential Equations and Stochastic Differential Equations, Numerical Optimization
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optimisation, distributed-parallel-GPU optimisation (e.g. pagmo2), Taylor-based numerical integration of ODEs (e.g. heyoka), differential algebra and high order automated differentiation (audi), quantum
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of linear algebra and numerical optimization Understanding of statistical modeling and inverse problems is desirable Experience with programming languages like Python, MATLAB, or C++ Joy in dealing with