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data combined with focussed innovation in statistical and computational methods including machine learning to advance our understanding, treatment, and prevention of human disease. Information
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students to analysing multimodal learning data (e.g., individual as well as collaborative verbal interactions, student gestures, task and activity sequences) and evaluating long-term learning outcomes. As
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science, medicine, or a related field. Excellent programming skills in Python and/or R. Experience with data curation, large-scale datasets, and genetic and machine learning methods. Interpersonal skills and
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projects involve large-scale population cohorts, single-cell genomics, statistical genetics, functional genomics, machine learning, and clinical translation. We are a diverse and international team
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analysis, causal inference with machine learning, and deep learning for various health-related domains. The Global Pathogen Analysis Platform (GPAP) is a new international initiative to strengthen global
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methods in practice. This includes the ability to design, implement, and critically reflect on computational approaches such as machine learning models, large language models, and/or advanced data
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-principles physical knowledge with data-driven learning to enable continuous, autonomous system oversight. Research objectives The project pursues two interconnected research directions: Continuous
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, or a closely related discipline. You have experience with spectroscopy and chemometrics and at least some of the following areas: data acquisition, equipment construction, machine learning, and image