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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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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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must be inferred from the data. The overall goal is to develop reliable and robust statistical methods that can contribute to scientific understanding and inform decision-making and public policy
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education across the entire product development lifecycle, from early concepts to industrialization. Together with industry, we develop methods and tools that enable robust, sustainable products while
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Innovation Network center SWE-WIN, which aim to advance resilient, robust, and energy-efficient wireless networks capable of supporting emerging services. The research focuses on resource management and
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classification and multi-layered environment mapping - Digital twin generation for natural environments - Semantic scene-understanding in natural environments for robust decision-making - Learning-based
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batteries. The project focuses on investigating how manufacturing precision and integrated metrology can reduce process variability, minimize process-induced errors, and improve production robustness and how
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. Yet robust simulation tools for evaluating these risks across diverse demographics remain lacking. This project proposes to bridge this gap by transferring state-of-the-art methods from humanoid
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focusing on securing the software, we target principled security mechanisms that provide robust protection against large classes of attacks. Modern software applications are almost never built from scratch
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in Umeå and through international collaborations, enabling robust findings across multiple datasets. Work Tasks We offer stimulating and meaningful work in collaboration with doctoral students and