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) programme and research school Data-driven life science (DDLS) uses data, computational methods and artificial intelligence to study biological systems and processes at all levels, from molecular structures
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that require more knowledge. In order to both sustainably use and safeguard forest biodiversity, a coherent basic science research program is needed that addresses large and complex issues and develops new
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intelligence to study biological systems and processes at all levels, from molecular structures and cellular processes to human health and global ecosystems. The SciLifeLab and Wallenberg National Program for
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, from molecular 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
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and first cycle studies specialising in environmental science are carried out at the department. First cycle studies include a bachelor's and a master's programme, as well as stand-alone courses 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 data
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at the Faculty of Medicine will be part of the faculty-wide doctoral education programme. The doctoral programme comprises 25 credits and is offered in two study variants: 25 credits spread over 8 terms (a total
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%) Complete coursework relevant to the PhD program Your profile Required qualifications MSc degree in Environmental Engineering, Chemical Engineering, Civil Engineering, Biotechnology, or a related field
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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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sustainable future through materials science. Read more: https://wise-materials.org All early-stage researchers recruited into the WISE program will be a part of the WISE Graduate School https://wise