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PhD Position on Machine Learning Detection of Positive Tipping Points in the Clean Energy Transition
resilient decarbonization. Your research will combine time-series analysis, supervised and unsupervised learning, and explainable AI methods to uncover the dynamic patterns preceding technological
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teaching. At Utrecht University , the various disciplines collaborate intensively towards major strategic themes . Our focus is on Dynamics of Youth, Institutions for Open Societies, Life Sciences and
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from geothermal energy to CO₂ and hydrogen storage. While individual clogging mechanisms have been studied extensively, the dynamic interplay between physical particle deposition, chemical precipitation
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Programme? Not funded by a EU programme Is the Job related to staff position within a Research Infrastructure? No Offer Description Challenge: Distinguish noise and signal in data streams Change: Refined
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flexible IAM that links climate dynamics with socio-economic processes. You will develop an inventory of critical feedbacks, translate them into model representations, and conduct uncertainty analyses
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job in a dynamic and ambitious university, in an interdisciplinary setting and within an international network. You will work on a beautiful, green campus within walking distance of the central train
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research and inspiring teaching. At Utrecht University , the various disciplines collaborate intensively towards major strategic themes . Our focus is on Dynamics of Youth, Institutions for Open Societies
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the representation of feedbacks, tipping points, and extreme events in the MIMOSA model, a flexible IAM that links climate dynamics with socio-economic processes. You will develop an inventory of critical feedbacks
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Challenge: Distinguish noise and signal in data streams Change: Refined asymptotic analysis of stochastic processes Impact: Faster detection of anomalies and reliable uncertainty quantification Job
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volatile geopolitics. Shortages, trade frictions, and financial mismatches can stall otherwise viable tipping dynamics and establish carbon-intensive lock-ins. This PhD will develop an agent-based inspired