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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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, normalization, dimensionality reduction) to downstream interpretation (differential expression, gene set enrichment, and cell type annotation). Implement Machine Learning Approaches, including deep learning
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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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will be working closely with machine learning researchers and on scientific projects using AI, so a background in machine learning is highly desirable. In general, candidates should have a doctoral
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research / molecular biology. The proposed project is intended for an MSc-level person who wishes to acquire PhD degree. PhD Trainees in my laboratory typically graduate within 3-4 years with competitive
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advancement of modern artificial intelligence (AI) methods in drug discovery and development. An ideal candidate will have strong background in machine learning, computational chemistry, or related fields, with
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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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enthusiastic doctoral researcher with: A master’s degree (or equivalent) in conservation science, ecology, geography, or other relevant fields. Previous experience with digital data analysis, modelling, machine