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Field
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safety-critical domain, reducing hallucinations and improving robustness and trustworthiness are essential. This PhD targets principled ways to detect, analyze, and mitigate hallucinations in video-based
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) offer a solution by integrating physical laws with complex data. However, their effectiveness is still hindered by uncertainties related to both data and models. Strengthening their robustness requires
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mechanical problems [1-3]. Special attention will be devoted to the integration of physics-informed constraints in the learning process to ensure robustness, interpretability, and extrapolation capabilities
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mechanical problems [1-3]. Special attention will be devoted to the integration of physics-informed constraints in the learning process to ensure robustness, interpretability, and extrapolation capabilities
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vital insights to the global debate on transparency and regulatory adaptation, ensuring that the integration of AI into corporate reporting aligns with robust ethical standards and improved risk
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approaches for Dutch EHR texts, minimizing information loss while ensuring robustness, transparency, and practical usability. You will work at the intersection of epidemiology, clinical research, data science
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Programme? Not funded by a EU programme Is the Job related to staff position within a Research Infrastructure? No Offer Description Are you interested in improving the interpretability, robustness and safety
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see advert: https://www.strath.ac.uk/studywithus/postgraduateresearchphdopportunities/business/economics/socialvalueofelectricalnetworkinvestment/ Study modes eligibility: Full-time Fee Status: Project
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is desirable but not essential, alongside clear potential to develop as an independent researcher. For full details, see advert: https://www.strath.ac.uk/studywithus
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3D environments. • Design of a robust control architecture to ensure autonomous navigation using information from the optical localization system (development of estimation algorithms, use of observers