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Campus (LLEC). Development of physics-informed and graph-based machine learning methods for energy system monitoring, forecasting, and planning Data analysis considering uncertainties, missing data
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tend to hallucinate facts. By contrast, other AI technologies, such as knowledge graphs and formal reasoning engines, are able to reason reliably, but are less good at handling ambiguity. This PhD
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to provide reliable predictions and support effective decision-making. The aim of the PhD project is to address these gaps by developing theoretical and computational tools that combine game theory, graph
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Research Framework Programme? Not funded by a EU programme Is the Job related to staff position within a Research Infrastructure? No Offer Description The PhD thesis will be funded within the framework
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, Create insightful graphs and other graphical tools to present outcomes back to farmers, Develop reporting for specific groups of farmers that are for instance in a specific supply chain or a learning
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multivariate systems during extreme events like floods, heatwaves, or financial crashes. This PhD position offers a combination of theoretical research and the implementation of new statistical methodology and
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), within the ATLAS group. The PhD student will join a large-scale international collaboration and participate in the activities of the ATLAS experiment at CERN. The work will focus on the analysis of data
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Application deadline: All year round This 3.5-year PhD project is fully funded and home students are eligible to apply. The successful candidate will receive an annual tax-free stipend set at
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Peterson). "Statistical field theory applied to complex networks” "Quantum geometrogenesis – Graph theoretic approaches to building spacetime” web page For further details or to discuss alternative project
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al., “Deep Transfer Learning for Fault Diagnosis”, IEEE Transactions on Industrial Electronics, 2020. • Zhang C. et al., “Graph Neural Networks for Power Systems”, Electric Power Systems Research, 2023