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- manufactured Ni₃Al intermetallic alloys that integrates residual stress considerations directly into the design process. The research will focus on the development of topology optimization algorithms capable
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with sufficient accuracy using fast parametric algorithms and machine learning (ML) models instead of time-consuming simulations to evaluate scenarios for improving the user experience along cycling and
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engagement—particularly within ESG disclosures—its implementation is currently challenged by ethical concerns regarding data privacy, algorithmic bias, and cultural resistance. This project aims to contribute
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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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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