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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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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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(Stockholm, Sweden) and the candidate will benefit from a strong (inter-)national network of collaborators in protease biology and computational proteomics. The successful candidate for this position will join
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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
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in English. A completed doctoral degree in a relevant subject area, such as molecular biology, immunology, or a related field. Strong ability to initiate, develop and conduct high-quality research
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into consideration. We are looking for someone with a doctoral degree in computational physics, applied mathematics, computer science, bioinformatics, structural biology, or a related field. Documented experience in
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postdoctoral researcher will be involved into discussions within a broad range of fields including computational, medicinal and organic chemistry. Requirements PhD degree in biochemistry or structural biology
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. Our research is focused on cell biology, spatial proteiomics and machine learning for bioimage analysis. The aim is to understand how human proteins are distributed in time and space, how this affects
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microenvironment. Research funding has been secured through a prestigious grant from SciLifeLab, a Swedish national center for advanced research and one of Europe’s leading molecular biology laboratories, driving
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, bioinformatics, computational biology, applied mathematics, or a closely related field. This eligibility requirement must be met no later than the time the employment decision is made. Strong background in ML and