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The Faculty of Science invites applications for a POSTDOCTORAL RESEARCHER IN MACHINE LEARNING FOR NATURE CONSERVATION starting from August 2025 or as agreed. The Postdoctoral Researcher will be
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. We require the candidate to have documented experience in either large-scale genomics data analysis with computational or approaches/biostatistics, or machine learning/deep learning. Experience with
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dysfunction. An ensemble of multi-scale computational approaches (molecular dynamics simulations, quantum chemistry, machine learning) are applied to study the mechanistic aspects of biomolecules in great depth
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of large-scale genomic data sets is a requirement for this position. Experience with data integration, machine learning, network science, cancer biology, and/or gene regulation is considered an advantage
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research on emotion AI, learning processes and perception engineering for AI-based solutions e.g. in education and nursing. HI research combines the strengths of both humans and machines, emphasizing mutual
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strong publication record and a solid background in computational RNA biology, particularly in alternative splicing regulation, are our top priorities. Expertise in machine learning and human omics data
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, are our top priorities. Expertise in machine learning and human omics data analysis is highly advantageous. The ideal candidate should demonstrate a high level of independence while also valuing