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About the opportunity: 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
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, computational methods, and artificial intelligence to study biological systems and processes at all levels, from molecular structures and cellular processes to human health and global ecosystems. The SciLifeLab
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intelligence to study biological systems across scales, from molecular processes to ecosystems. The DDLS program aims to train the next generation of data-driven life scientists and build internationally leading
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Department of Animal Biosciences Technology Description of the doctoral project: Data-driven life science (DDLS) uses data, computational methods and artificial intelligence to study biological
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the national Data-Driven Life Science (DDLS) program. About the position and the project As an industrial PhD student, you will be employed by the startup company PredictMe AB while being formally enrolled as a
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. The project is based on a close collaboration with researchers at the Department of Immunology, Genetics and Pathology (IGP) at Uppsala University and SciLifeLab . About the DDLS research program The PhD
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to provide the skills needed to analyze, develop, and contribute to the interdisciplinary area of artificial intelligence, autonomous systems and software. Through an ambitious program with research visits
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-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 to human
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research combining computational biology with experimental validation. Selection The selection among eligible candidates will be based on their ability to successfully complete the PhD studies. Key criteria
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Collaboration Conduct research together with academic, industrial, and healthcare partners in a highly collaborative environment. Who we are looking for A highly motivated PhD candidate excited by computational