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focused on modeling and simulations of turbulent mixed-phase clouds. This interdisciplinary project encourages collaboration with experts in atmospheric physics. The successful candidate will contribute
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transferable and interpretable models for tabular data, efficient learning paradigms for medical imaging, and causally grounded and identifiable representation learning. You will have great freedom to influence
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at: https://www.umu.se/en/department-of-computing-science/ Project description and working tasks The project will develop privacy-aware machine learning (ML) models. We are interested in data driven models
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postdoc to join our team at the Division of Applied Mathematics and Statistics, Department of Mathematical Sciences, and contribute to research on stochastic and statistical models for large-scale shape
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Join us to pioneer next-generation generative models that accelerate molecular dynamics. We seek a postdoctoral researcher to develop AI surrogates for molecular dynamics (MD), slashing
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understanding of slag modification routes and their implications for material performance. The research combines thermodynamic modelling, laboratory-scale experiments, and advanced slag characterization
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and automated floor-plan recognition, to fill data gaps and harmonise information from disparate sources. Learn more and watch our project video here: https://sb.chalmers.se/digital-material-inventories
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forest systems.The position offers the opportunity to work across scales and methods, integrating quantitative risk modelling with empirical field data and applied demonstrations to support climate
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the integration of AI components transforms the nature of software systems (SE4AI). From an architectural perspective, the research investigates how the inclusion of AI elements—such as retrainable ML models, LLMs
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educational programs, we are now seeking a postdoctoral researcher to work on privacy for data-driven models and high-dimensional data. The position is full-time for two years, starting on 1st April 2026, or as