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of engagement. Read more at: 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
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Engineering, we are seeking a researcher with a strong interest in developing and applying machine‑learning methods for materials design, in particular steel design. The position is part of our growing research
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of algorithms, machine learning, optimization, scientific software development and high-performance computing. The division is also an important part of the eSSENCE strategic collaboration on e-science and of
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machine learning for the next generation of AI models – uncertainty-aware foundation models, generative models and world models – with the support of competent and friendly colleagues in an international
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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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development practices (e.g., XR, VR, computer graphics, and programming); and play in participatory, learning, and societal contexts (e.g., STEAM education and live-action role-playing). Link Application
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experimental studies, mechanistic modelling, time-resolved data analysis, and machine learning to develop and validate predictive models linking process signals to reaction behaviour, progressing from controlled
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. The postdoctoral researcher(s) will join an international research environment at Umeå University, including Stat4Reg (www.stat4reg.se ), which develops statistical and machine-learning methods for register data
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embodied interaction); game design and development practices (e.g., XR, VR, computer graphics, and programming); and play in participatory, learning, and societal contexts (e.g., STEAM education and live
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-driven approach for optimizing the growth of semiconductor materials by combining machine learning with a physics-based understanding of the growth process. Doping and processing of ultra-wide bandgap