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Learning Lab. The successful deployment of statistical models and AI solutions relies heavily on the quality of underlying model assumptions and the learning algorithms they employ. The design of loss
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algebras, tensor categories, lattice models of statistical physics, conformally invariant random processes, formalization of mathematics (preferably in Lean). The working language of the group is English
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Conversion and Systems at the Aalto University School of Engineering invites applications for Doctoral Researcher in Sustainable Renewable Energy Engineering, Modeling and Optimization Department of Energy and
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the Helsinki Probabilistic Machine Learning Lab. The successful deployment of statistical models and AI solutions relies heavily on the quality of underlying model assumptions and the learning algorithms
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information on the tenure track model, career path and assessment criteria at the University of Helsinki, please see https://www.helsinki.fi/en/about-us/careers/academic-careers/university-helsinki-tenure-track
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air quality modelling. The project is funded by the Jane and Aatos Erkko Foundation. Research tasks include Developing future emission scenarios for various urban environments; Updating and applying
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to large models. In addition to research work, persons hired are expected to participate in teaching following the standard practices at the department. Your experience and ambitions The position requires
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multiscale and multiphysics modelling, mechanical testing and microstructure characterization, and digital measurement and quality control techniques. Among the applications in green processes and methods
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). The project aims to match dietary fibre types to prevalent functional gut microbiome subtypes to increase fibre consumption without digestive symptoms. The project aligns with the overall aim of our research
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. The work aims at modeling carbon flows, developing solutions for removing and storing greenhouse gases into the built environment, and developing the life cycle assessment method for quantifying multiple