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aims to develop responsible transport appraisal methods that consider multiple performance metrics simultaneously, with a special emphasis on fairness to weigh different circumstances, constraints, and
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on preferences the candidates will work along one (or more) of the following different directions: theoretical foundation involving quantitative models (e.g. stochastic, timed weighted, hybrid automata) and logics
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modular, scalable, and transparent control algorithms suitable for real-time implementation across different vehicle platforms. - Contribute to theoretical developments in stochastic model predictive
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of the North Sea. Existing automated detection algorithms will be used to identify seals and porpoises. The results will be used to create seasonal acoustic presence maps, investigate differences in community
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data requirements, and lower costs for large-scale modelling tasks. PINNs enhance predictive capabilities and efficiency by combining data-driven methods with physical principles. Unlike traditional
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are using ferroelectric memories, which can calculate AI algorithms from the field of deep learning in resistive crossbar structures with extremely low power consumption and high speed. Furthermore, we
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. -Machine learning code generation for autonomous translation of payload data semantics. -Dictionary learning and algorithms for translation between major data modeling languages. -Model-based System
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communication systems which have different performances under different channel conditions. - Providing high-speed underwater communication, for industrial applications in real time underwater communications
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algorithm. Design methods: Develop novel control methods for power electronic converters feeding electric machine Simulation: Learn advanced simulation tools such as Ansys to simulate and analyze the effect
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detection and classification algorithms using measured and/or simulated data, such as current pulses from cable faults (breakdown), partial discharges and external noise. In addition to being part of