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Machine Learning group at TDB and SciLifeLab (Associate Professor Prashant Singh), which develops methods and software for simulation-based inference, generative models and robust machine learning, together
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in large pre-trained models (vision-language models), generative models (flow matching, diffusion), simulation-based inference, and robust and active learning. The group has a wide network of
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models for complex data, including temporal data. We are interested in both data-driven models as well as models built from synthetic data. Within privacy, we are interested in different types of privacy
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focuses on the development of GPU-accelerated, high-fidelity thermal runaway simulation models for lithium-ion battery cells, modules, packs, and complete battery systems. Thermal runaway is a chain
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. Experience and expertise in human mobility simulation and prediction with agent-based modeling and deep learning techniques. Proficient in Python programming for geospatial data processing, modeling, and
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stimulating multidisciplinary research environment focused on regulatory mechanisms that enable cells to retain their identity.We employ Drosophila and human cell culture models, genomics, genetics and
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. Beyond Discrete Mathematics, the Department of Mathematics and Mathematical Statistics carries out research in computational mathematics, financial mathematics, mathematical modeling, analysis, machine
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. The project explicitly seeks to move beyond static “average-day” demand modelling towards dynamic, behaviourally rich and policy-relevant simulations of how different groups adapt under changing conditions. The
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candidates whose expertise falls within one or more of the following areas: computational and mathematical modeling, statistical modeling, machine learning, network science, bioinformatics, applied mathematics
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models for complex data, including temporal data. We are interested in both data-driven models as well as models built from synthetic data. Within privacy, we are interested in different types of privacy