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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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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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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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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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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
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, and develops various tools for bioimage analysis, mostly using machine learning and AI-based models. As a postdoc you will conduct research using various methods in cell-and molecular biology, but
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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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, incorporating their own ideas and experience in computer vision, machine learning, and related fields, to further visualization and interpretation of molecular images. Our research environment focuses
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on development of novel computational methods with state-of-the-art machine learning for gaining fundamental insights into healthy and diseased human tissues of the heart, cardiovascular system, and