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Field
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Are you eager to support exciting research with ultracold atoms and trapped ions? Do you want to participate in the creation of complex machines that have never existed before? Do you enjoy sharing
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, modelling, and AI to turn scientific research into real-world impact. You’ll join an open, supportive environment that fosters learning and professional growth. Job requirements Must haves: Master’s degree in
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, Industrial Engineering, or related discipline; Affinity and/or experience with computer programming, statistical learning, and optimization techniques; A good team spirit and feel at home at the intersection
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, tools, or artistic creations, humans instinctively explore the unknown in order to acquire information about it, to make sense of it, to act on it, and to appreciate what is in front of them
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(or equivalent) in Computer Science, Artificial Intelligence, Engineering or a closely related field; Solid background in machine learning and/or evolutionary optimisation; strong programming skills (Python/C
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, development of data (pre-)processing pipelines, and machine learning model training to identify relevant biological states of the liver (e.g., healthy, recovering, not healthy). The (soft) sensor development
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such as case weighting, anomaly detection, and model-based prediction (e.g., geostatistics and machine learning), using auxiliary geospatial or remotely sensed data. Quantifying uncertainty and correcting
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degree in Computer Science, Artificial Intelligence, Data Science, or related field; Solid background in machine learning, deep learning and foundation models such as Large Language Models; Strong
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-ACCENT project aims to fill this gap by combining insights from cognitive psychology, neuroscience, AI engineering, human-computer interaction and social science, with lifespan perspectives. Using advanced
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Christmas and 1 January; multiple courses to follow from our Teaching and Learning Centre; a complete educational program for PhD students; multiple courses on topics such as time management, handling stress