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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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prospection with the FLAIR's strengths in developing new multi-objective deep reinforcement learning algorithms, to support decision makers under uncertainty. VUB team:Prof. dr. Ralf Vandam (AMGC), Prof. dr
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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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simulation methods and scientific software development. The successful candidate will contribute to research and development on: EMT simulation of future power systems. Simulation methods for converter
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developing generic tools that take a declarative problem description and automatically compute an optimal solution to it. Often, users specify their problem in a high-level, human-understandable formal
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closely with prof. Roel Leus on advancing the state of the art in decision diagram-based methods for combinatorial optimization, including the development of new theory, algorithms, and high-impact
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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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antiviral target-discovery by uncovering this terra incognita. To that end, we developed high-throughput, multiplex, high-content multiparametric phenotypic antiviral assays. These allow to screen hundreds