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, longitudinal patient and population registries and biobanks. Project description Large language models (LLMs) enable the extraction of clinical information from unstructured medical text. However, current LLM
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relative to one another. Regarding scientific expertise The following qualifications will be assessed: Required: Experience and scientific achievements in AI applications involving large-scale medical data
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are looking for an Industrial PhD Student to join an ambitious project focused on building foundational models of human biology using large-scale, multimodal data. In this role, you will work at the
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experience in areas such as advanced image analysis, data structuring and data integration, and large language models (LLMs) with applications in biological and biomedical research. The successful candidate´s
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development, high-throughput data analysis, and working with large population-based cohorts and clinical biobanks. The student will learn how to scientifically assess the study quality, perform appropriate
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involves data sources from large population-based cohort, national registry-based cohort, and clinical imaging cohort. The student will learn how to conduct high-quality epidemiological studies including
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data with computational modeling Programming skills in Python, R, or another relevant language. Interest in machine learning, statistical modeling, structural bioinformatics, or analysis of large-scale
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infrastructures that supports large-scale data and hypothesis-driven research in the field of molecular biosciences. SciLifeLab (www.scilifelab.se ) operates nationally, engaging all major Universities of Sweden
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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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of molecular networks in cancer to advance precision medicine. By integrating high-throughput data (e.g. transcriptomics, proteomics, metabolomics) with prior knowledge of molecular interactions, we construct