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exposure AI based algorithms to optimize the performance of integrated terrestrial and non-terrestrial networks (coverage, capacity, energy, exposure) Digital twin operation: dynamic environment-aware
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algorithms. model and optimize end-to-end physical-layer performance, hardware non-idealities, and overall power consumption. focus primarily on space and security topics (satellite communications, reliable
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Foundation Machine Learning topics, including designing new architectures, and training algorithms Implement World Models for simulation on multimodal data (vision + tabular, vision + graph) Evaluate
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with advanced control methods. The research will therefore explore RL algorithms that not only operate optimally against such competing objectives but also effectively under uncertainty, changing
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adequate solutions in a timely manner experience in data management technical expertise in the integration of hardware, software and algorithms is a plus you have an interest in plant science and molecular
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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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absorption assessment via FDTD using EM field data Detection and localization algorithms of dynamic users using wireless signal parameters and sensor fusion Generation of Digital Twin city model that is able
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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
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/impact, (3) brief approach (models, algorithms, datasets), (4) evaluation plan, and (5) alignment with IDLab research on reinforcement learning at the University of Antwerp. The selection committee reviews
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. Proposal is maximum 4 pages, excluding references. The research proposal should include: (1) problem statement and motivation, (2) expected novelty/impact, (3) brief approach (models, algorithms, datasets