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The Danish Center for Hadal Research (HADAL) at the Department of Biology, University of Southern Denmark, invites applicants for 5 Ph.D. positions in deep-sea biogeochemistry and microbial ecology
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strong background in machine learning and/or computer vision is required, along with solid programming skills in Python and experience with deep learning frameworks (e.g. PyTorch). Prior research
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- Knowledge in programming in Python or R - Familiarity with machine learning or deep learning methods is a plus - Interest in plant genomics, evolutionary biology, or comparative genomics - Proficient in
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analysis utilizing methods rooted in artificial intelligence (i.e. machine learning and deep learning). The analysis will be the basis for developing a predictive model to help select the most optimal method
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ambitious Novo Nordisk Foundation Data Science Collaborative Programme, “Synthetic health data: ethical development and deployment via deep learning approaches (SE3D)” which is a collaboration between Head of
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programme at the Faculty of Science . The ideal candidate has a background in or experience with one or more of the following topics: Advanced deep learning architectures Mathematical foundations of machine
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complex interaction patterns that may carry important biological information. By integrating deep learning, genome-wide simulations, functional genomics, and large-scale biobank data, AI:GENOMIX aims
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motivated candidate with a strong background in statistics and/or machine learning. Areas of particular interest include, but are not limited to: Causal Discovery and Causal Inference Extreme Value Theory
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Professorhip grant, which you can learn more about here: https://www.cnap.hst.aau.dk/lundbeck-professorship As a PhD fellow your tasks include: Conduct research under the supervision of senior CNAP staff members
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prediction outputs. The first PhD will work on data fusion, feature extraction, and model development ranging from baseline approaches (e.g., gradient boosting) to deep learning architectures. The work also