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of Information Technology website . The project will be led by Professor Carolina Wählby , within the Image Analysis unit of the department’s Vi3 division, working alongside researchers developing numerical and
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. To meet the general entry requirements for doctoral studies, you must: Hold a Master’s degree in computer science, image analysis and machine learning, engineering, data sciences, applied mathematics
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to have a background in engineering and/or an affinity for mathematical modelling and sustainability assessment methods such as life cycle assessment. Affinity with pharmaceutical manufacturing or chemical
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technology, or applied mathematics, with a specialization in energy systems analysis, or energy economics. Strong written and verbal communication skills in English. The following experience will strengthen
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) biological knowledge about GRNs from bioinformatics and system biology, (b) graph theory and topological data analysis for network modeling from mathematics, and (c) robust machine learning (ML) and GenAI from
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Science, Statistics, Applied Mathematics, or a related field. • Strong background in convex analysis, statistical machine learning (reinforcement learning, LLM and generative modeling), stochastic modeling and
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Uppsala University, Department of Information Technology Are you interested in developing new image analysis and machine learning methods for improved cancer understanding, diagnostics, and
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collected in January 2025 aimed at capturing submesoscale flows and the mixing, stirring and water-mass transformations they generate. This PhD will lead the analysis of physical and biogeochemical data
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Max Planck Institute for Demographic Research (MPIDR) | Rostock, Mecklenburg Vorpommern | Germany | about 1 month ago
in individuals’ other life domains (e.g. partnerships, education, employment, housing) and to changes in their socio-economic context. The project will involve analysis of individual-level data from
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Vacancies Two PhD positions on Flexible and User-adaptive statistical inference Key takeaways You will develop a mathematical framework for multiple testing, enabling flexibility in study design