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PhD Position on Machine Learning Detection of Positive Tipping Points in the Clean Energy Transition
Infrastructure? No Offer Description Develop machine learning models to detect early signs of abrupt shift towards clean energy technologies and make climate action adaptive to this information. Job description
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PhD Position on Machine Learning Detection of Positive Tipping Points in the Clean Energy Transition
Develop machine learning models to detect early signs of abrupt shift towards clean energy technologies and make climate action adaptive to this information. Job description Positive tipping points
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approach to ecosystems, addressing abiotic (soil and water quality) and biotic factors (ecology and evolution of plants, animals, and microorganisms), and the interplay between those. The IBED vision
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Your job Are you fascinated by how public space is managed, maintained, coordinated and sustained over time? Do you wonder why municipalities, provinces, and water authorities so often struggle
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» Process engineering Environmental science » Global change Environmental science » Water science Researcher Profile First Stage Researcher (R1) Country Netherlands Application Deadline 17 Oct 2025 - 21:59
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pathways. This research project will develop the process flow model for selected technology configurations, including electrified units, hydrogen-based processes (instead of carbon-based for reduction
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. Foreseen challenges include data harmonization, key relationship extraction, scalability, metadata structuring, predictive modeling, and data quality assurance. The research will integrate structured and
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for pavement lifetime prediction, primarily in Dutch conditions. Foreseen challenges include data harmonization, key relationship extraction, scalability, metadata structuring, predictive modeling, and data
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the system dynamics as dictated by demand-side management. Input from consortium industry partners on component, operational and other costs will be used to validate the techno-economic models and quantify
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used to validate the techno-economic models and quantify the risks of business case development. A strong collaboration with consortium members in industry and academia is key. Research Context The EU