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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
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(100%) doctoral scholarship holder in the field of in vitro gametogenesis within the context of fertility preservation. Position You will work actively on the preparation and defence of a PhD thesis in
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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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. You have a strong interest in machine learning/artificial intelligence. You have experience with deep learning. You have experience with reinforcement learning (preferred). You have experience with
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and cooperation. You have a strong interest in machine learning/artificial intelligence. You have a strong interest in graph-based learning (e.g., graph neural networks). You have experience with deep
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attention to quality, integrity, creativity and cooperation. You have a strong interest in machine learning/artificial intelligence. You have a strong interest in graph-based learning (e.g., graph neural
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scholarship holder in the field of Machine Learning and Systems Modelling for Advanced Air–Liquid Interface Cell Culture Models. Position You will work actively on the preparation and defence of a PhD thesis
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of Electronics-ICT at the Faculty of Applied Engineering are looking for a full-time (100%) doctoral scholarship holder in the field of Machine Learning and Systems Modelling for Advanced Air–Liquid Interface Cell