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. Ensuring resilience in such scenarios is critical for safety-critical applications including environmental monitoring, infrastructure inspection, and emergency response. This PhD will develop a mathematical
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. Nevertheless, scientific challenges still need to be addressed before such foundation AI models can be used in a loop for decision-making in distribution systems. This PhD position aims to address some of these
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distribution systems. This PhD position aims to address some of these scientific challenges, including how to properly incorporate power systems physics into the foundation AI model architecture, as well as how
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. The PhD candidate, funded by the UKHSA, will work with the IID3 study team and the UKHSA using study data. The aim is to understand how laboratory methods can affect the detection of pathogens and how
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UiO/Anders Lien 1st August 2026 Languages English English English PhD Research Fellow-Continuous Experience Recognition for Distributed Co-creative Immersive Systems Apply for this job See
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Research Fellow-Continuous Experience Recognition for Distributed Co-creative Immersive Systems Apply for this job See advertisement About the position We invite applications for a PhD Research Fellow
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Inria, the French national research institute for the digital sciences | Villers les Nancy, Lorraine | France | 2 months ago
Framework Programme? Not funded by a EU programme Reference Number 2026-10252 Is the Job related to staff position within a Research Infrastructure? No Offer Description This PhD thesis will take place in
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with many unresolved challenges. Two of these challenges revolve around data availability and operational uncertainty. This PhD research project aims to investigate how distribution systems can exploit
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Job description The Delft University of Technology is hiring a doctoral candidate on the subject "Distribution systems flexible operation under uncertain and incomplete information". The Ph.D
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: Department of Biology (Professor Laura Pollock ) Position Summary: Develops and implements species distribution models (SDMs) to predict the geographic distributions of tropical plant species using