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programming languages such as Python and experience with deep learning frameworks (e.g., PyTorch, TensorFlow) is highly desirable strong interest in interdisciplinary research combining imaging, machine
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will be based in Odense, under the primary supervision of Prof. Ricardo J. G. B. Campello , but they will be expected to also work closely with other PhD students, postdocs, and collaborators both from
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, Ultrasound and Vibration, Aircraft Structures, Damage Assessment, Structural Health Monitoring, Structural Health Prognosis, Bayesian Statistics, Machine Learning Informal enquiries prior to making
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Project Title: Intrinsically-aligned machine learning In a truly cross-disciplinary effort, this project, funded by the Leverhulme Trust and in collaboration with the University of Manchester, will leverage
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machine learning tools for the efficient analysis of the experimental data. For more information, visit our web page www.soft-matter.uni-tuebingen.de We are looking for a motivated PhD student to contribute
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using X-ray and neutron scattering. One of the research areas is the development of machine learning (ML) based approaches to efficient analysis of the vast data amounts generated in the scattering
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machine learning processing of the spectroscopic data • The optical design and development of novel custom spectroscopic sensors benefitting from freeform optics. • Integration of the in-situ
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standard track (30 months at IMT Atlantique + 3 months at University of Waterloo, Canada where the PhD student will stay 3 months at Prof. Ricardez’ lab. + 3 months at a non-academic partner). 1.1 Domain and
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Vacancies PhD position Social dynamics of energy communities in the Dutch energy transition Key takeaways Social dynamics shape behaviours that can accelerate or hinder the Dutch energy transition
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Associação do Instituto Superior Técnico para a Investigação e Desenvolvimento _IST-ID | Portugal | 29 days ago
with setting up a streamflow forecasting system in Portugal and the advancement of scientific knowledge in machine learning probabilistic hydrological forecasting and decision-making optimized to act on