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comfort throughout the year in a Nordic climate? Is it possible to predict dynamic outdoor thermal comfort with sufficient accuracy using fast parametric algorithms and machine learning (ML) models instead
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incorporate optimized machine learning algorithms, support standardized IoT protocols, and be validated in laboratory and semi-industrial environments. The project contributes to smart maintenance strategies
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integrating modeling, machine learning (ML), and advanced control methodologies. The research will focus on designing AI-driven algorithms to assess battery health, predict degradation trends, and optimize
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multidisciplinary approaches, including advanced bio-imaging, image analysis, cell biology, genetics, and molecular biology. Find out more about our research group here: https://tymri.ut.ee/en/content/developmental
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statistics of mean and extreme wind events over the Baltic Sea region. Tellus A, 67, 29073. https://doi.org/10.3402/tellusa.v67.29073 Björkqvist, J.-V., Lukas, I., Alari, V., van Vledder, P.G., Hulst, S
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friction. Coastal Engineering, 53(2), 149–165. https://doi.org/10.1016/j.coastaleng.2005.10.005 Dean, R. G., & Walton, T. L. (2010). Wave setup. In Y. C. Kim (Ed.), Handbook of Coastal and Ocean Engineering
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http://www.ut.ee/en https://tuit.ut.ee/en https://matter.ee/ Street Ülikooli 18 E-Mail kairi.herik@ut.ee sven.oras@ut.ee STATUS: EXPIRED X (formerly Twitter) Facebook LinkedIn Whatsapp More share options
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changes to hydrometeorological forcing. Geomorphology, 414, 108383, https://doi.org/10.1016/j.geomorph.2022.108383 Eelsalu, M., Viigand, K., Soomere, T., Parnell, K., 2024a. Systematic analysis