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to algorithmic decision-making in transport, welfare, policing, and urban planning – exposes the deeply sociotechnical nature of contemporary urban governance. In response to growing concerns about discrimination
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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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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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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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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