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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
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training to the Swedish life science research community, with a strong national and international collaborative network. The main responsibilities are to: Carry out advanced data analyses within nationally
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research software or similar initiatives. Meritorious: Interdisciplinary contributions made in collaboration with medical researchers. Regarding technical expertise The following qualifications will be
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multi-omics integration with advanced machine learning, including artificial neural networks, to predict disease-relevant splice variants across cardiometabolic diseases. By leveraging extensive meta
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good software engineering habits — modular, well-documented, reproducible code. Are comfortable working in interdisciplinary teams and can communicate effectively across computational and experimental
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offered by these programs will facilitate the group members’ nationwide networks and multidisciplinary collaborations. Working in a young team, group members will benefit from first-hand guidance from
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educating researchers and technical staff within life science Documented experience as a system developer, including technologies for containerisation such as Docker Excellent ability to communicate fluently
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, machine learning, data integration, and scientific communication. As part of the DDLS Research School, the student will also participate in national courses, seminars, and networking activities within data
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information for union representatives. Doctoral Student’s network (Students’ union on KTH Royal Institute of Technology) Contact information for PhD chapter . To apply for the position Apply for the position
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systems, neuroscience, and safety and security. The Division of Systems and Control enjoys a wide network of strong international collaborators all around the world, for example at the University of Oxford