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patients develop a durable response. Many researchers are investing efforts to understand the complexity of anti-cancer immunity and develop diagnostic approaches that accurately predict therapy benefit and
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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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, molecular communication, and systemic molecular networks, and to identify how these processes change across physiological and disease states. Through this work, you will develop predictive and biologically
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enjoys a wide network of strong international collaborators all around the world, for example at the University of Oxford, the University of Melbourne, and the University of California, Los Angeles. We
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Medicine (WCMTM) and SciLifeLab in the University of Gothenburg. and is tightly connected to a broad network of international collaborators. Duties Develop and perform high-resolution spatial transcriptomics
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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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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