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of machine learning (ML) and quantum many-body physics. We are also happy to work with experts in one of the two fields who are committed to learning the other. Moreover, we look for interest in developing
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Candidate Human-Centered Interpretable Machine Learning (1.0fte) Project description In recent years, practitioners and researchers have realized that predictions made by machine learning models should be
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Apply now The Faculty of Science, Leiden Institute of Advanced Computer Science,is looking for a: PhD Candidate Human-Centered Interpretable Machine Learning (1.0fte) Project description In recent
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://www.universiteitleiden.nl/en/science/mathematics . What you bring The successful candidate is expected to have: A master in statistics, (applied) mathematics, machine learning or a closely-related quantitive discipline (to
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organismal domains and data modalities, making use of state-of-the-art methodologies such as systems/network analysis, artificial intelligence and machine learning and/or computational modelling approaches
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national and international events and conferences; proficient in general computer skills and experience with electronic collaboration tools; and willing to learn the Dutch language. The working language of
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, artificial intelligence and machine learning and/or computational modelling approaches. Moreover, the candidate will have a leading role in expanding and professionalizing the growing computational biology
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. The research will combine computational modeling (e.g., NLP, machine learning, deep learning) with human-centered research (e.g., user studies, experimental design, qualitative analysis). We are looking not only
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emotion safety is crucial; Design interventions to reduce bias and improve fairness and safety in human-AI interaction. The research will combine computational modeling (e.g., NLP, machine learning, deep
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enhance real-time decision-making in road traffic management. The project aims to bridge the gap between recent advances in AI and machine learning, in particular, multimodal and instruction-tuned