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algorithms capable of operating in large, partially observed spatial domains to infer efficient, interpretable strategies for estimating archaeological potential, that capture distinct criteria including
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, operational, and societal constraints. The postdoc will work at the interface of reinforcement learning and computational epidemiology, focusing on the development of new reinforcement learning algorithms
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layers. Within this environment, we will design and evaluate new reinforcement learning algorithms capable of operating in large, partially observed spatial domains to infer efficient, interpretable
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) rigorous evaluation of algorithmic improvements.For some inspiration on this topic, see the CertiFOX project page: https://www.bartbogaerts.eu/projects/CertiFOX/ Another long-standing objective is to
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-dominated grids, protection systems, and wide-area dynamics. Scalable algorithms and numerical methods for large-scale simulation. Scientific software and software architectures for next-generation simulation
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constraints. The postdoc will work at the interface of reinforcement learning and computational epidemiology, focusing on the development of new reinforcement learning algorithms. The project will consider
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cleaning, filtering, etc.).•Expertise in data fusion and relevant algorithms (deep learning, generative AI, kernel methods, Bayesian methods). •Preferably, experience with high-content imaging or cell