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facility. Good understanding of how pre-analytical and laboratory methods can affect data quality. Quality control and analysis of large-scale proteomics data. Experience working in an accredited environment
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Data-driven life science (DDLS) uses data, computational methods and artificial intelligence to study biological systems and processes at all levels, from molecular structures and cellular processes
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learning, using large-scale Neuropixels recordings combined with computational modelling. The project is embedded in a national network of data-driven life science research spanning eleven Swedish
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recruiting an outstanding and ambitious postdoctoral researcher in computational biology to advance the integration and modeling of large-scale microscopy data using modern machine learning approaches
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named in a way that clearly shows their content. Applications must be received by: 2026-05-21 Information for International Applicants Choosing a career in a foreign country is a big step. Thus, to give
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· Develop and apply transformer-based foundation models and machine learning methods for large-scale epigenetic datasets · Integrate longitudinal data and biological prior knowledge into AI models · Actively
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transformation and large green investments in northern Sweden create enormous opportunities and complex challenges. For Umeå University, conducting research about – and in the middle of – a society in transition
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symptoms emerge. The group combines large population-based and twin cohorts with longitudinal blood-based biomarkers, multi-omics data, and advanced epidemiological methods. The group is part of the research
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by developing data-driven approaches to identify and prioritize isoform-specific therapeutic targets, enabling a new level of precision in RNA-based treatments. The project will combine large-scale
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life science technologies with data and AI expertise. Computational methods and artificial intelligence applied to large-scale molecular data are transforming the study of biological systems at all