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, immunology, or similar. Previous practical experience with high-parametric spatial proteomics platforms Experience in data analysis and visualization in Python of high-parameter immunofluorescence microscopy
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attention will be given to research skills. Direct and documented experience with liposome preparation of model membranes and analysis of bilayer structural parameters from cryo-EM data are required
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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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methods for understanding biological form, function, and evolution. The project combines computer vision, machine learning, genomics, and biomechanics, and involves large-scale multimodal datasets including
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to demonstrate documented proficiency in English. You have knowledge and expertise in computer vision and/or medical image analysis, deep learning as well as mathematics. You have substantial expertise in
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independently. Merits: Education or training in computer vision, machine learning, deep learning, bioinformatics, advanced microscopy, cell biology, or RNA biology. Education in mathematical statistics
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methods. Good computer skills. Meriting Qualifications Documented theoretical or practical experience in structural biology and/or mass spectrometry. Experience in project management and communication with
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processing, computer vision, machine learning, deep learning and neural networks, as well as courses in python, GPU programming, mathematical modeling and statistics, or equivalent. The University may permit
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Linux command-line environment and on high performance computer clusters Excellent communication skills in both written and spoken English are required, as is ability to collaborate with scientists
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prior experience in one or more of the following areas Särskilt meriterande krav Experience in using computer clusters and distributed systems. Experience in virtualized environments, e.g., Docker