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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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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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Willingness to combine wet-lab experimental work and computational analysis Basic knowledge of statistics and/or programming (R, Python, or similar) Strong motivation to learn new techniques across
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collaboration with KU Leuven and Maastricht University, we train over 900 students to become creative and problem‑solving legal professionals. We do this through assignment‑ and problem‑based learning and always
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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
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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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: Master’s degree in Electrical Engineering Ranked within the top 10% of their class in MSc and BSc, and have exceptional grades Good background in deep learning with familiarity in model training, inference
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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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that do not have this background, are strongly motivated to acquire the relevant skills during the early phases of the PhD, supported by training and in close collaboration with experts in single-cell