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program 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 cellular processes
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complex biological processes. This project combines timely analytical challenges with deep rooted applications in life science. We are looking for a candidate with a PhD in either engineering/computer
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learn experimental and computational approaches to tackle fundamental biological questions with medical relevance using innovative system-wide techniques. You will work on an exciting multidisciplinary
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. Selection criteria Essential PhD in computational biology, bioinformatics, or computer science with a focus on human gut microbiome data Excellent written and oral English communication skills. Significant
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biomedicine, biomolecular technology, and biodiversity converge working synchronously under one roof. With one of the highest concentrations of bioinformaticians and computational biologists in Europe, Campus
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part of SciLifeLab BioImage Informatics Facility (BIIF) as described below. The Vi3-Division gathers a unique combination of expertise in computerized image processing, human computer interaction, and
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the Data-driven Life Science Fellows program. The group, led by Wei Ouyang, focuses on building AI systems for data-driven cell and molecular biology. We are seeking a postdoctoral researcher in cell biology
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. The position is open to computational scientists with an interest in single-cell metagenomic analysis. Applications will be accepted until December 11th, 2025. About the position Bioinformatic methods for single
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fellow in data-driven cell and molecular biology. For more information about us, please visit: DBB and MBW Data-driven life science (DDLS) uses data, computational methods and artificial intelligence
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, the Faculty of Science and the regional university healthcare, within a broad range of medical topics and disease areas, as well as cutting-edge infrastructures , high-capacity computational resources