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and cognitive science, investigates the processes of AI-augmented scientific discovery. SCI-AI involves ethnographic fieldwork conducted in frontier AI labs, and cognitive modeling of problem-solving
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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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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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). 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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veterinary diagnostic imaging. The assessment is always conducted as an overall assessment. For more information on the tenure track model, career path and assessment criteria at the University of Helsinki
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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
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. The handling of unseen languages remains without real solutions, despite the research community having made a few practical excursions, such as using trash language models or confidence thresholds
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. For more 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