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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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thesis in the field of continual reinforcement learning. Continual reinforcement learning studies how agents can learn across a sequence of changing tasks, environments, or objectives while retaining
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thesis in the field 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
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Luxembourg. The successful candidate will join the Security, Reasoning and Validation (Serval) research group and work on a research project related to the application of machine learning for official
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looking for a full-time (100%) doctoral scholarship holder in the field of machine learning for circular polyurethane design. Position You will actively work on the preparation and defence of a PhD thesis
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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
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methods tailored to ILC. The PhD researcher will fine-tune and benchmark pathology foundation models using multi-site H&E and immunohistochemistry whole-slide images. The aim is to learn representations
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histopathology and clinical information to learn robust cross-modal representations for diagnostic prediction. It pursues two integrated objectives: (i) to develop generative and explainable AI approaches