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will involve three short visits to partner laboratories to learn cell delivery methods, immunologic characterization of cells and tissues, live cell tracking, and multi-omics data analysis methods. As a
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the flexibility of neural methods. If successful, the work has the potential to advance applications such as automated theorem proving, knowledge-graph inference, and causal analysis. The Department of Computing
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relevant data criteria and data sources along the textile value chain using multi-criteria decision analysis. Develop frameworks and models for standardised data collection, data sharing and data use between
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: Experience in method development, working with spatial data, and GIS Experience with univariate and multivariate analysis and working with large datasets Experience with working independently and organizing
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functional proteomics. Experience with cellular infection studies in a BSL2 environment, as well as sample preparation for quantitative proteomics. Strong skills in quantitative proteomics data analysis and
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methods, including GIS and spatial analysis. Applicants are invited to visit our departmental website to review the kinds of research we do in the department (https://www.uu.se/en/department/human-geography
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for efficient last-mile deliveries with a focus on climate and flexibility. Using advanced modeling and data analysis, you’ll create solutions that make final deliveries smarter and more sustainable. Your work
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Electrical Engineering, Engineering Physics, Applied Mathematics, Computer Science, Communications or similar. Strong background in mathematical analysis (multivariable calculus, probability, linear systems
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algorithms for Bayesian machine learning with applications in e.g., medical image analysis. The doctoral student position is offered within the machine learning project “The Challenges for Machine Learning in
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acclimate to a changing world and how we can breed better plants. Read more about our benefits and what it is like to work at SLU at https://www.slu.se/en/about-slu/work-at-slu/ Integrative genomic analysis