49 data-"https:" "https:" "https:" "https:" "https:" "https:" "Helmholtz Zentrum Dresden Rossendorf" positions at SciLifeLab in Sweden
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. Location: SciLifeLab , Solna https://ki.se/en/research/research-areas-centres-and-networks/research-groups/uncovering-the-molecular-and-physical-principles-governing-early-embryonic-division-and-nuclear
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and evolutionary dynamics. The positions are based in Assistant Professor Lisandro Milocco’s research group at Stockholm University and SciLifeLab, within Sweden’s national Data-Driven Life Science
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or senior staff scientist. More information: https://www.scilifelab.se/researchers/simon-koplev/ Qualifications Requirements A doctoral degree or an equivalent foreign degree. This eligibility requirement
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Reference number REF 2026-0110 Are you passionate about pushing the boundaries of genomics? We are looking for a Doctoral student to join an exciting project at the intersection of genomics and data
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of Medical Biosciences, which offers an international, collaborative, and open-minded research environment. Please visit the lab’s webpage for more information: https://erdemlab.github.io . The Erdem research
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guided by supervisors. Doctoral studies end with a thesis and a doctoral degree. More about being a doctoral student at LTH on lth.se. https://www.lth.se/english/study-at-lth/phd-studies/ Subject and
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with nano-LCs. Performing data analysis and interpretation using established proteomics software and tools, such as Proteome Discoverer, Spectronaut, Skyline, Byonic, Perseus and R-based Shiny
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seeking a skilled and results-oriented Senior Research Engineer motivated by supporting researchers in other fields with excellent technical service. Further information about the service facility is
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-how in molecular biosciences. NBIS is also the Swedish node in the European infrastructure ELIXIR for biological information. We are now strengthening our capacity in our NBIS support unit for cell and
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recruiting an outstanding and ambitious postdoctoral researcher in computational biology to advance the integration and modeling of large-scale microscopy data using modern machine learning approaches