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team of experienced researchers in imaging, machine learning, oncology, and pathology. We do not discriminate on the basis of sex, gender, belief, culture, place of birth or occupational impairment when
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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
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Machine Learning, and has over 30 PhD students, postdoctoral researchers and faculty members working on a broad variety of deep learning, computer vision, and foundation model subjects, like self-supervised
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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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& Image Sense lab (VIS lab), at the University of Amsterdam. VIS lab is a world-leading lab on Computer Vision and Machine Learning, and has over 30 PhD students, postdoctoral researchers and faculty
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pathology applications, including the assessment of kidney biopsies. The innovative application of machine learning in clinical settings creates a vibrant and inspiring research environment. You will be part
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this position, the chosen candidate will conduct research on how machine learning techniques or XAI can be leveraged by heuristic algorithms, or conversely, how heuristics can be enhanced by incorporating machine
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for science). We do so by bringing together a diverse team of PhD candidates who will focus on three key areas: 1. Probabilistic and differentiable algorithms for machine learning; 2. Programming language
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programming applications (e.g., experimental design, machine learning for science). We do so by bringing together a diverse team of PhD candidates who will focus on three key areas: Probabilistic and
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identification and machine learning. The key challenge is striking a balance between, on the one hand, modelling the physical, dynamic and nonlinear behavior of the components with sufficient physical accuracy