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Field
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when using closed, proprietary models, where model weights, training data, and internal representations are inaccessible. The PhD project will therefore investigate how trustworthy agentic AI systems can
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renewable energy use, energy security, and the reliable operation of hydro-dominated power systems. The project will focus on how AI can support advanced optimization models for hydropower and energy-system
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, execution, and evaluation of laboratory experiments Take part in and initiate research activities within high voltage and high current technologies Prepare and contribute to applications for externally funded
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to investigate the potential of using Implicit Neural Representation (INR), a class of neural networks, for reconstructing MR images directly from MR signals. By incorporating a physical model of the
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for identifying concentration, floe size, geometry, and possibly stage of development. The plan is to build models so that radar measurements alone can be used to populate, as far as possible, the Stage
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the field, model development, remote sensing analyses, analyses of timber production, economic and policy data and documents, development of forestry cost functions, or surveys of forest owners. The project
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research and artificial intelligence can be combined to improve planning and operational decision making under uncertainty. The project addresses challenges that arise when traditional optimization models
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probabilistic and deterministic reliability criteria with market operation in a single security-constrained optimal power flow model, and combining optimal power flow analysis with reliability analysis methods
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in one or more of the following: railway engineering, infrastructure maintenance, rolling stock, condition monitoring, sensor data analysis, statistical modelling, machine learning, life-cycle cost
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on AI-empowered asset management using drone inspections and available asset management historical data. The aim is to enhance the asset and network resilience of ports by training an AI model on ferry