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Infrastructure? No Offer Description Join our ERC SPHINX Team to model how climate risks ripple through housing markets. Help uncover when homes become stranded assets and fuel systemic impacts. Help shaping
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collection of wearable sensor data in daily life. Working together with AI experts, you will develop models to extract insights into the progression of key PSP symptoms, based on high-frequency movement and
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strategies for programming, modeling, and integrating reconfigurable/spatial architectures, such as FPGAs and ML accelerators, within heterogeneous ICT ecosystems. Reconfigurable and Spatial hardware, such as
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strong energy systems knowledge, electricity, heat, and gas netwrok modelling & simulation, and research software development skills that want to develop cutting-edge knowledge and software for the energy
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transduction could enable a new class of transistor circuits as well as novel sensor and transducers. Your PhD project will focus on both the fundamental physics of these devices with modelling, fabrication, and
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strong energy systems knowledge, electricity, heat, and gas system components and flexibility modelling & simulation, and research software development skills that want to develop cutting-edge knowledge
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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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Description Develop models to trace global material and financial flows, uncover bottlenecks, and assess how resource and investment constraints shape pathways to a feasible clean energy transition. Job
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AI methods and tools that capture the complexity of the port call process and handle uncertainties caused by the energy transitions. In collaboration with the other researchers, this will include
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make extensive use of low-fidelity simulations which can provide fast but inaccurate solutions depending on the flow complexity. To close this gap, this PhD will explore machine-learning (ML) methods