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compositions. Initially, supervised learning models such as random forests, gradient boosting, and neural networks will be used to predict composition outcomes based on both literature scrapping and in-house
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energy network research, including: demand management and flexibility, digital twinning, data analytics, smart grid ICT architectures and systems integration in multi energy systems. The latter specializes
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outside academia. Your immediate leader will be the Head of Department. About the project This position is part of the Marie Skłodowska-Curie Actions Doctoral Network (MSCA-DN) project MetaExplore
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regularly in highly ranked international journals, and is often mentioned in both international and domestic media. The center has a large international network, and regularly organizes workshops and
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coordination failures reshape decarbonization pathways. Your research will combine methods from network analysis and agent-based modelling of economic systems to trace how international material and financial
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prestigious Marie Skłodowska-Curie Actions Doctoral Network SHIELD (Strategies for Healing Implant-associated infections and Enhancing Longevity in Devices) and contribute to cutting-edge research aimed
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(SFDI) and also from our custom-built photoplethysmography (PPG) sensor. Applicant should have experience in time-series processing with appropriate AI models (recurrent networks, LSTM) and experience in
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areas, including the Fens and Romney Marshes, depend on complex networks of drains, rivers, and pumps to regulate water levels. These drainage catchments are crucial for protecting homes, sustaining
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PhD Position on Machine Learning Detection of Positive Tipping Points in the Clean Energy Transition
Positive tipping points in the innovation and diffusion of clean energy technologies can greatly accelerate progress towards a net-zero energy system. Yet, their emergence and timing remain difficult
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offered within the Marie Skłodowska-Curie QuNEST – Quantum Enhanced Optical Communication Network Security doctoral training program (https://qunest.eu ) and the Villum Investigator Program: Power-Efficient