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of approaching reconstruction and variability analysis. The project combines applied mathematics, computational imaging, and structural biology. You will develop algorithms, implement and test software tools, and
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. Requirements University degree in bioinformatics, computer science, IT, or a related field. At least three years of documented work experience in bioinformatics-related systems development. Excellent knowledge
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The SciLifeLab and Wallenberg National Program for Data-Driven Life Science (DDLS) invites applications for 22 postdoctoral fellowships starting in autumn 2026. This call marks the launch of a new
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. The research environment is embedded in Linköping University and closely connected to SciLifeLab and the national DDLS program. https://liu.se/en/employee/bjofo78 https://www.nsc.liu.se/systems
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internationally outstanding research in the life sciences. Project description We seek two highly motivated postdoctoral researchers to develop new mathematical and computational methods for modeling developmental
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an outstanding and ambitious postdoctoral researcher in computational biology to pioneer understanding and modeling of tissue architecture using single-cell and spatial transcriptomics data. The focus will be
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group is part of the SciLifeLab and Wallenberg National Program for Data-Driven Life Science (DDLS), which aims to recruit and train the next-generation of data-driven life scientists and to create
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analysis and computational modelling. This position offers the opportunity to contribute to innovative experimental research within a dynamic and collaborative environment, advancing high-impact studies
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. The position is based in the Computational Genomics Research (CGR) Lab, within the Data Science and AI division. About us The Department of Computer Science and Engineering , a joint department of Chalmers and
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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