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health and global ecosystems. The SciLifeLab and Wallenberg National Program for Data-Driven Life Science (DDLS) aims to recruit and train the next generation of data-driven life scientists and to create
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studies. About the DDLS program Data-driven life science (DDLS) combines data, computational methods, and artificial intelligence to study biological systems from molecular structures to human health and
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cellular processes to human health and global ecosystems. The SciLifeLab and Wallenberg National Program for Data-Driven Life Science (DDLS) aims to recruit and train the next generation of data-driven life
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programme. The doctoral programme comprises 25 credits and is offered in two study variants: 25 credits spread over 8 terms (a total of 4 years) or over 12 terms (a total of 6 years), starting each autumn and
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structures and cellular processes to human health and global ecosystems. The SciLifeLab and Wallenberg National Program for Data- Driven Life Science (DDLS) aims to recruit and train the next generation of
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health and global ecosystems. The SciLifeLab & Wallenberg National Program for Data-Driven Life Science (DDLS) aims to recruit and train the next generation of data-driven life scientists and to establish
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to human health and global ecosystems. The SciLifeLab and Wallenberg National Program for DataDriven Life Science (DDLS) aims to recruit and train the next generation of data-driven life scientists and to
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global ecosystems. The SciLifeLab and Wallenberg National Program for Data-Driven Life Science (DDLS) aims to recruit and train the next generation of data-driven life scientists and to create globally
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the time the applicant is admitted to the PhD program at the Department of Biochemistry and Biophysics. You meet general entry requirements if you have completed a second-cycle degree, or completed courses
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Program for Data-Driven Life Science (DDLS) aims to recruit and train the next generation of data-driven life scientists and to create globally leading computational and data science capabilities in Sweden