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intelligent decision-making for autonomous driving. Ultimately, the project aims to advance the development of safer, smarter, and more efficient automated vehicles by creating a robust perception framework
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. The work includes both theoretical modeling and experimental validation, addressing development of software-defined power electronics, droop control adaptation to weather conditions, integration with
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optimization, and the interplay between microstructure properties and the LPBF technique. Key aspects of the project will include the: (1) Development of novel and next generation functional materials for LPBF
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of Ni#Al intermetallic-based superalloys. The project will focus on predicting thermal histories, residual stress development, and crack susceptibility during additive manufacturing. Using finite element
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of electrical machines, like switched reluctance machines. The main emphasis will be on the development of switched reluctance machine control methodology, which will consider additively manufacturing machine
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academic advancement and managerial application. List of crucial references Huang, M.-H., & Rust, R. T. (2018). Artificial Intelligence in Service. Journal of Service Research, 21(2), 155–172. https
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, collaborate with international academic and industry partners, and contribute to the development of open-source tools that shape the future of autonomous mobility. Research field: Production and materials
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). The work offers opportunities to publish in high-ranking journals and conferences, collaborate with international academic and industry partners, and contribute to the development of open-source testing
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predict how policies can support cities such as Tartu and Võru to achieve sustainable urban development goals. Research field: Building and civil engineering and architecture Offered by: School
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to developing a quantitative decision support system for environmental, social and health impact assessment (ESHIA) aligned with the targets of the UN Sustainable Development Goals (SDGs). Implementation