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PhD Position on Machine Learning Detection of Positive Tipping Points in the Clean Energy Transition
to anticipate and manage. This PhD will develop a machine learning module to detect early warning signals of positive tipping points from techno-economic data, helping policymakers design adaptive strategies
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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
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these technologies can only read DNA fragments of limited length. We enable biological interpretation of these sequencing data sets by developing algorithms based on graph theory, discrete optimization and machine
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38.0 Is the job funded through the EU Research Framework Programme? Not funded by a EU programme Is the Job related to staff position within a Research Infrastructure? No Offer Description PhD
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, particularly the role of modal couplings in energy transfer and dissipation, remains a fundamental challenge. This PhD project offers a unique opportunity to tackle these two complementary perspectives. In
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preferences. This PhD position addresses these challenges by developing generative AI models that can produce complete ship designs. Your responsibilities To be able to generate preliminary, regulation
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decision support tools. These mathematical models help battery owners make strategic decisions, such as when to charge and discharge or on which electricity markets to focus. In this PhD trajectory, we
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energy needs, reducing levelised cost of energy (LCOE) and decarbonisation. Optimal utilisation of sustainable heat requires storing large amounts of heat to account for seasonal supply and demand