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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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by the Fondation Paralysie Cérébrale—you will help develop a machine learning-based multimodal prediction tool for CP diagnosis and long-term outcomes. This tool will integrate advanced clinical
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Help revolutionize one of the world’s most carbon-intensive industries by developing cleaner, hydrogen-based processes for ironmaking. As a PhD researcher at TU Delft, you’ll contribute
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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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doctoral programme is designed to train world-class researchers in Strategic Management and Entrepreneurship, with a strong emphasis on quantitative methods. We combine in-depth knowledge of key topics in
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that are interested in developing computer models of the composite human neuro-muscular system that combine detailed musculoskeletal geometries, muscle-tendon models and neural control pathways (e.g., CPG-like and
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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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. Together they guarantee a highly relevant and scientific approach to topical administrative and social issues. The bachelor- and master programs of DPAS are built on this profile and develop students
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The Molecular Plasmonics group at the Eindhoven University of Technology has a PhD position in the field of metasurfaces for single-molecule biosensing. This project aims to develop a new generation
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to develop the next generation of PCM composites extracted from upcycled polymer pyrolysis waste streams and thermally conductive fillers. In this PhD project we adopt a molecular modelling approach with which