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machine learning solutions to optimize the component lifecycle directly contributing to a more circular economy. Information In the manufacturing landscape, determining whether a component should be
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predictive digital rock physics and permeability evolution models from µCT data using machine learning and computational tools (PuMA/CHFEM/MOOSE) validated against experimental observations Bridging scales
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PhD Position on Machine Learning Detection of Positive Tipping Points in the Clean Energy Transition
. 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 for rapid and
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). PhD students in our department receive excellent training and ample opportunities for feedback. In addition to standard required course work, students typically take courses in machine learning, (micro
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. Please do not contact us for unsolicited services. Where to apply Website https://www.academictransfer.com/en/jobs/356217/phd-position-machine-learning-m… Requirements Additional Information Website
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computational neuroscience, connectomics and/or Machine Learning is appreciated. Our goal perform connectome analysis on neuroimaging data\integrate connectome findings across multiple speices work on developing
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. This paradigm enables algorithms to securely access and process data within the environments where it resides, supporting federated learning for training machine learning models without moving sensitive or large
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. Until now, specific EN fingerprints of localized corrosion are determined manually. This is a tedious procedure that requires considerable expert knowledge. Artificial intelligence or machine learning (AI
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the SCM section will fill a maximum of one PhD positions this year that can be on any of these topics. Machine learning for stochastic last-mile deliveries In recent years, stochasticity has received
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, this PhD will explore machine-learning (ML) methods to significantly reduce the turnaround time of SRS, thus enabling their use for industrial design processes. By combining state-of-the-art numerical