162 genetic-algorithm-computer "Integreat Norwegian Centre for Knowledge driven Machine Learning" Postdoctoral research jobs in Sweden
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the application of machine learning to optics, touching upon aspects of computational electromagnetism and inverse design. Information about the project and division Structured optical materials, such as
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setting. In this environment, our research group focuses on combining novel genome engineering tools (e.g., CRISPR-based) and computational algorithms to enable regenerative cell therapies. Now, we are
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computational algorithms to enable regenerative cell therapies. Now, we are seeking a highly driven postdoctoral researcher to contribute to our ambitious mission. Division The Division of Biomolecular and
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Application Deadline 28 Sep 2025 - 22:00 (UTC) Type of Contract Temporary Job Status Full-time Is the job funded through the EU Research Framework Programme? Not funded by a EU programme Reference Number 304--1
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funded by a EU programme Is the Job related to staff position within a Research Infrastructure? No Offer Description At the Division of Clinical Genetics and within the research group led by Thoas Fioretos
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analysis of complex, longitudinal, and high-dimensional data (e.g., immunometabolic profiles, clinical data, biomarkers). Development and application of predictive models and algorithms for diagnostics
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extensive interests in population genetics and human evolution, providing numerous opportunities for collaboration with postdocs and PhD students working on related projects. The program is also part of
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Deadline 10 Oct 2025 - 21:59 (UTC) Type of Contract Other Job Status Full-time Is the job funded through the EU Research Framework Programme? Not funded by a EU programme Is the Job related to staff position
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Deadline 10 Oct 2025 - 21:59 (UTC) Type of Contract Temporary Job Status Full-time Is the job funded through the EU Research Framework Programme? Not funded by a EU programme Is the Job related to staff
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project to study genetic regulatory variation and its link to molecular, cellular and organismal phenotypes using a systems genetics approach. The project is fully computational, and potential approaches