16 learning-"https:"-"https:"-"https:"-"https:"-"https:"-"https:" PhD positions at University of Antwerp
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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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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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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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. You have excellent English language skills. You have notions of Dutch, Finnish or Italian, or are willing to acquire these. You pay attention to details and act with attention to quality and integrity
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of reinforcement learning (RL) for industrial process control and optimisation. The research focuses on developing RL methods that can support decision-making and control in complex industrial systems while
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researchers. You will (learn to) compete for internal or external research funding. You will assist in teaching lab sessions of relevant courses at bachelor or master level (circa 10% of your time). Profile You
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. Knowledge of data analysis, optimization, and machine learning techniques is a plus. Knowledge of machine learning libraries (e.g., PyTorch or TensorFlow), SDR hardware (e.g., USRP) and software (e.g