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Field
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structure refinement. Experience working at large-scale research facilities is required. You have strong data analysis skills and experience handling complex experimental datasets. Preferred qualifications A
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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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this work, software will be developed to efficiently handle large volumes of heterogeneous data, from ingestion to processing and output of results. The software will be built within a Linux-based high
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is close. Our cohesive campuses make it easy to meet, work together and exchange knowledge, which promotes a dynamic and open culture. The ongoing societal transformation and large green investments in
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with such models. Experience with machine learning methods applied to biological data. Familiarity with large language model APIs and frameworks (e.g., Claude/Anthropic API, OpenAI API, LangChain
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energy models (UBEM). The work also involves using several large language models (LLM) to manage workflows, process data, develop user and building archetypes, conduct simulations, and analyze results and
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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