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that leverage state-of-the-art AI methods (deep learning, generative AI, Bayesian modelling, active learning, etc.) to combine cellular imaging data, chemical compound structure, viral genomes and other omics
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of redox flow batteries. Within the ELCAT research group you will find yourself in an excellent research environment consisting of more than 50 researchers and equipped with state-of-the-art lab
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by prof. Jonas Hereijgers and entails cutting edge research. The aim of RECHARGE is to develop the next generation of redox flow batteries. Within the ELCAT research group you will find yourself in
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For more than 50 years, the Vrije Universiteit Brussel has stood for freedom, equality and solidarity, and this is very much alive on our campuses among students and staff alike. At the VUB, you will find a
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of redox flow batteries. Within the ELCAT research group you will find yourself in an excellent research environment consisting of more than 50 researchers and equipped with state-of-the-art lab
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properties predicted by Virtual-Coater™ can be translated into meaningful input parameters for battery modelling platforms. The ultimate objective is to establish a predictive modelling workflow capable
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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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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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. The project will consider different types of epidemic models, including spatial and individual-based models. Methodologically, the work will focus on multi-objective, hierarchical, and explainable reinforcement
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is of utmost importance that they be completely reliable. One of the central objectives of our research group is to develop methodologies and tools with which we can guarantee with 100% certainty