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primarily on the development, validation, and implementation of the Psychological Referral Optimization algorithm (PRO). PRO is a brief digital e-screening tool designed to support early identification, risk
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combining particle damping with architected metamaterial concepts enabled by Laser Powder Bed Fusion (LPBF). The proposed approach will employ optimized internal cavities acting as Tuned Mass Dampers (TMDs
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learning, with the aim of controlling the vessel in an optimal and efficient manner. In this context, control is formulated as a trade-off between different objectives, such as minimising energy consumption
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broad genomic screening and; Support hospitals in mitigating the impact of pandemics through optimized decision-making and data collection strategies. The project brings together experts in artificial
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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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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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Belgium. The overall objective of FLEX-SMR is to develop cost-optimal solutions for integrating SMRs into industrial energy systems. In particular, the project investigates how an SMR can be operated
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optimally combined to deliver models with extremely constrained compute and memory footprints without compromising performance. This includes training spiking neural networks with multiple plasticities
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research using Ipo8-/- mouse models on different genetic backgrounds by: 🫀 Phenotyping the arterial tree, incl. optimization of cutting-edge advanced imaging techniques 🦴 Characterizing craniofacial and
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camera systems. The main goal of this PhD position is to develop a generalized differentiable optical propagation model and optimize the related optics for realizing an incoherent holographic camera system