-
algorithms capable of operating in large, partially observed spatial domains to infer efficient, interpretable strategies for estimating archaeological potential, that capture distinct criteria including
-
, 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
-
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
-
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
-
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
-
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