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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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your academic CV, including: a transcript of study results. two reference letters with appropriate contact details. an English proficiency certificate. A short research proposal aligned with your
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research will address clinically relevant questions that arise directly from patient care, including treatment sequencing, optimization of systemic therapies, management of (high-grade) NEN. In addition, you
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. The candidate will design and analyse an energy consumption model for modern cellular base stations that accounts for contribution of multiple components and functions, such as idle static power, MIMO operation
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balancing multiple, often competing objectives. Industrial processes typically require trade-offs between objectives such as production efficiency, energy consumption, operational cost, product quality
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neurodevelopmental huntingtin (HTT) modulation across multiple spatial and temporal scales using dedicated mouse models of controlled HTT expression, combined with advanced non‑invasive functional MRI and