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diverse team of PhD candidates who will focus on three key areas: Probabilistic and differentiable algorithms for machine learning; Programming language implementation for high performance computing
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for donor kidneys. Central to this is the use of machine learning to evaluate the predictive value of biomarkers from various sources: donor-related data, perfusion fluid, and kidney biopsies. Kidney biopsies
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join us as a PhD candidate. You will work in a highly interdisciplinary group, at the intersection of physics, machine learning and theoretical neuroscience. Our group is focused on investigating
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work in our multi-disciplinary team, including AI-experts, MRI-physics experts, and clinical experts. You will conduct new academic research combining machine learning and medical physics. Your research
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are seeking a motivated PhD student to join our team working on realizing learning in novel physical materials, as part of a joint theoretical/experimental research project between AMOLF and the University
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, epidemiologists, clinicians and lab researchers, with expertise in the field of prediction modeling, longitudinal data analysis, statistics, data science, machine learning, AI, organoid models and cystic fibrosis
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recent advances in machine learning and browser automation, the project aims to provide tools, techniques and datasets to effectively address these threats. As a PhD candidate, you will play a key role in
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supervised by experts in combinatorial optimization, machine learning and fairness-awareness in algorithmic decision support, and the Eurotransplant headquarters in Leiden, where access to the domain expertise
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the attractiveness to the users, we need innovative designs where fixed and flexible services support each other. This necessitates a multidisciplinary approach bringing together optimization, machine learning and
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at the University of Amsterdam is inviting applications for a PhD position in mathematical machine learning. The position is part of the research project “Mathematical Foundations for Explainable AI”, funded by