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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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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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). 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
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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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, automated reaction mechanism generators, and high-resolution local scale air quality modelling. The project is funded by the Jane and Aatos Erkko Foundation. Research tasks include Developing future emission
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stability theory, modeling & identification, optimal control, certifiably safe & robust control, and learning for dynamics & control. The main task of the PhD student will be to develop sound data-driven
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by integrating computational mental health and computational social science, using large-scale social media analysis, smartphone-based sensing, and agent-based modeling. Combining macro-level patterns
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Sustainable Fibre Sources (fibrematch.eu), funded by the European Innovation Council (EIC). The project aims to match dietary fibre types to prevalent functional gut microbiome subtypes to increase fibre