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PhD Position on Machine Learning Detection of Positive Tipping Points in the Clean Energy Transition
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 to anticipate and manage
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of the way. With 3,200 m² of state-of-the-art lab facilities, housed in a museum with more than 40 million natural history objects, dedicated mentorship, and access to our broad international network
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(e.g. safety, norms, networks) influence active lifestyles; develop and test physical activity and destination choice models, integrating data from sources such as sensors, surveys, Google Street View
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! Job description This 4-year fully funded PhD position is part of the ERC Consolidator project “Systemic physical climate risk in complex adaptive economies” (SPHINX). The SPHINX-research program is made
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of buildings. This presents many opportunities, but also requires careful planning and operation to deal with emerging challenges such as network congestion. Recently, positive energy districts (PEDs), i.e
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efficient, adaptive use of multistable networks, offering an alternative to energy-intensive digital systems with rigid bits and separated memory and computation. In this project, we aim to design and realize
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an important contribution to solving complex technical-social issues, such as energy transition, mobility, digitalisation, water management and (cyber) security. TPM does this with its excellent education and
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an important contribution to solving complex technical-social issues, such as energy transition, mobility, digitalisation, water management and (cyber) security. TPM does this with its excellent education and
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) in the EU training network EXPLORA EXPLORA is a Marie Skłodowska-Curie doctoral network funded by the HORIZON 2020 framework. It will start on 1 February 2026, and within this network we have two
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-oriented policy is paradoxical. On the one hand, society needs solutions to be deployed urgently. On the other hand, mission-oriented innovation is highly complex, and rushing solutions may lead to