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collaborators in computational biology, machine learning, and imaging-based profiling. The position involves leading independent research projects at the interface of machine learning and biology, with a strong
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responsible for designing, optimizing, and fabricating these systems using bioprinting and cleanroom-based techniques. You will also measure molecular signatures and isolate cell subsets of particular interest
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developed the SPARCED pipeline to convert structured lists of species, parameters, and reaction types into an SBML (Systems Biology Markup Language) model file, and we created a model based on one
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Taheri Lab and the Lundberg Lab, funded by the Digital Futures (DF). The position is based at KTH Royal Institute of Technology and SciLifeLab (Science for Life Laboratory). KTH is Sweden’s largest
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program based in Sweden that been systematically mapping the localization of human proteins in cells, tissues and organs during the past 20 years. For this project, we map the subcellular distribution
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. Copy of diploma from your PhD. Translations into English or Swedish if the original documents have not been issued in any of these languages. Your complete application must be received at KTH no later
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, and ERC Consolidator grant (DETOXPEST) and aims to establish a public, cross-species “pan-terminome” knowledge base of plant proteoforms and to uncover how conserved proteolytic processing
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in life science. The creative and research-intense environment contributes to a broad knowledge-base as well as cutting-edge expertise. In addition, Uppsala University has a highly developed innovation
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Lund University Lund University was founded in 1666 and is repeatedly ranked among the world’s top universities. The University has around 47 000 students and more than 8 800 staff based in Lund
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genomics and Spatial Transcriptomics data analysis. The postdoctoral fellow will be based at KTH, Department of Gene technology, SciLifeLab (Science for Life Laboratory) Stockholm and will be supervised by