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Field
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the consequences of actions, and adapt reliably when the physical world changes? The project connects multimodal perception, reasoning and action with predictive learning and edge intelligence. Research directions
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investigate how AI methods and workflows can be designed and adapted to handle large, heterogeneous EO tasks efficiently and reliably. You will develop and evaluate approaches that address the trade-offs
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technologies to enable efficient, reliable, and intelligent communication between human operators and multiple robotic systems. Furthermore, the developed methods will be translated from theory to practice
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No. 101168344 Position: Doctoral Candidate – full-time, 12-month position Research Target: Trustworthy and Reliable AI for Cyber-physical Systems Deadline: 18 October 2026 Expected Start Date: 1 November 2026 How
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which technology and research fields are advancing. Furthermore, you will also extend the indicator beyond novelty toward quality assessment and validate the reliability, robustness, and fairness of LLM
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to the development of state-of-the-art nuclear-reaction models and evaluated nuclear-data libraries, supporting safe, reliable, and competitive technologies for both existing and future nuclear-energy systems, as
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rehydration scenarios. The project will assess whether salivary biomarkers can reliably detect changes in hydration status, recovery strain and electrolyte-related responses following exercise, heat exposure
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responsible for the HVT group’s laboratories, ensuring reliable and safe operation Support and guide PhD candidates and MSc students in planning and conducting experimental work Contribute to the design
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) benchmarks often prioritize leaderboard scores over practical utility, failing to capture how models behave in real-world, socially situated contexts. This PhD project treats evaluation methodology as a
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wakes reduce the energy production of downstream turbines and can increase structural loading, creating a major challenge for the efficient and reliable operation of large wind farms. A novel approach