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biomedical engineering, electrical engineering, machine learning, statistics, computer science, or a related area considered relevant for the research topic, or completed courses with a minimum of 240 credits
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. Experience with fluorescence-based characterization of lipid mixtures, including via imaging and spectroscopic methods, are a plus. Willingness to perform or analyze additional computational simulations
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KTH Royal Institute of Technology, Scool of Electrical Engineering and Computer Science Job description Cellular morphology reflects fundamental biological processes such as division
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. Experience in deep learning, computer vision, or neural network development. Experience with live-cell microscopy, fluorescence microscopy, or analysis of 3D/4D image data. Experience in cell biological
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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
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‑lab expertise, including Western blotting, DNA/RNA isolation, PCR and qPCR, and basic immunofluorescence imaging Experience in genetic engineering and genome editing (e.g. CRISPR). Demonstrated ability
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for therapy. To do this, we interrogate the spatial relationships between B and T cell clones and their immediate niches within tissues using our in-house developed spatial transcriptomics-based technology
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knowledge and experience: Educational background in Bioinformatics, Biostatistics, Computational/mathematical biology or a Biologically oriented engineering. Experience of working in Bash, and other relevant
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The Department of Gene Technology, at KTH Royal Institute of Technology, is looking to fill a position as Assistant Professor focused on innovative development and application of computational
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community of Digital Research Engineers. The offices also host computer infrastructure and machine learning/data science/research data management experts, who develop, build, and manage the local and national