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Field
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for studying nanoscale magnetic and magnet-superconductor hybrid systems, and implement these in numerical simulation codes. Interested candidates should send a motivation letter, a CV and the contact
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-particle physics, predictive modeling, and AI-enabled digital twins for magnetically confined fusion plasmas in tokamaks. The research will combine high-fidelity simulations, reduced transport modeling
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; expertise in several of the following: life cycle assessment, life-cycle cost analysis, pavement simulation, machine learning, deep reinforcement learning, and/or physics-informed modeling frameworks; and
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-time, and evidence-accumulation phenomena. Implement simulation, parameter-estimation, and model-comparison methods in Python, MATLAB, R, or related computational environments. Lead and co-author
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, qualification, and deployment of AI agents and models, Computational Fluid Dynamics (CFD) simulation codes, and Finite Element Method (FEM) based tools for nuclear energy (fission and fusion) applications
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a sequential decision-making process explored through computational simulation and deep multi-objective reinforcement learning. The project will investigate a simulation platform that reproduces
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systems such as multiple-scattering media and multimode fibers. The position involves developing advanced optical experiments, conducting numerical simulations, and engaging closely with theoretical aspects
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sources for LISA. The position is particularly suitable for candidates with a background in one or more of the following areas: stellar and binary evolution; hydrodynamical simulations of stellar
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project of the VUB, introduces a new research framework that treats archaeological prospection as a sequential decision-making process explored through computational simulation and deep multi-objective
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, technological, and societal challenges of computer science. The researcher will join the SocSIM-K (Social Simulation and Knowledge) research team at LIG, whose work is carried out within the framework