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machine learning-driven digital twins for predictive combustion modeling. The research program will cover a wide range of e-fuels (H₂, NH₃, CH₃OH, DME, OME) and their applications in industrial furnaces
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enrolled in ULB’s 4-year doctoral programme, in which they acquire many other important career skills. PROFILES Prof. Maes is looking for three highly motivated, intellectually curious, and collaborative
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, microscopy, spectroscopy, pore-structure characterization, geochemistry. You are interested in interdisciplinary research at the interface of materials science and microbiology and are motivated to acquire
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considered a strong asset. Experience with Deep Learning and Artificial Intelligence is considered a plus. Excellent proficiency in the English language is required, as well as good communication skills, both
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kinds of shops, on flights, in petrol stations, amusement parks...) and Ecocheques; Nursery near campus, discount on holiday camps; The space to form your job content and to continuously learn through our
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actively on the preparation and defence of a PhD thesis in the field of explainable reinforcement learning (XRL). Explainable reinforcement learning aims to make decisions, policies, and learning processes
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actively on the preparation and defence of a PhD thesis in the field of continual reinforcement learning. Continual reinforcement learning studies how agents can learn across a sequence of changing tasks
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of continual graph learning. Continual graph learning studies how graph neural networks can learn from a sequence of evolving tasks, graphs, or distributions while retaining previously acquired knowledges
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for using AI to develop social engineering attempts. This project combines human subject research of learning and decision making, Human-Computer Interaction, and the advancement in AI methods
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plasticity consists in learning dendritic, synaptic, or axonal temporal delays to enrich the network’s spatiotemporal dynamics. This research project will thus explore how these different mechanisms can be