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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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full-time, three-year fixed-term contract starting in September 2025 (or later by agreement). The position includes a six-month trial period. Postdoc applicants must hold a doctoral degree by the start
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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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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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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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investigation and will develop an advanced computer modeling framework. By simulating processes at various scales, from the atomistic to continuum, we aim to reveal how temperature and saturation fluctuations
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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