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macro- scales at IJL, and to train machine learning models to predict the microstructure evolution at larger scales and longer times at SIMAP lab and Laboratoire Analyse et Modélisation pour la Biologie
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, test and measurement methodologies for electronic modules, system engineering, data pre-processing and database indexing/analytics for dashboarding/visualisation, embedding machine learning algorithms
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Doctoral Researchers (PhD students) to work on deep learning methodologies for machine and robot perception. These positions are funded by the Horizon Europe project OPERA (Open Perception, Learning, and
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materials using statistical mechanics, molecular simulations, and machine learning. Expectations Candidates will be responsible for: Developing multi-scale modeling methods for polymeric materials, using
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, engineers, PhD students, and postdoctoral fellows, at the interface between fundamental research, technological development, and experimental validation. Where to apply Website https://emploi.cnrs.fr/Offres
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institutional memberships to the Center for the Integration of Research, Teaching and Learning. To learn more visit: https://engineering.osu.edu/faculty-development . Learn more about the College of Engineering
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requirements and focusing on data-value maximisation. This project will utilise innovative machine learning methods and tools from process systems engineering to simultaneously optimise product quality and the
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brings complementary and/or additionally new expertise including AI/Machine Learning-based methodologies that can be developed for virtual ligand screening, reverse virtual screening (target fishing) and
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knowledge and proven capacity of data analytics and machine learning. *Excellent programming in Python, R, SQL. Have experience with tools such as Google analytics, AWS, Looker, Tableau, or similar
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reproducible analysis workflows Familiarity with computational models of vision and machine learning methods (for example CNNs, deep generative models, encoding models) is preferred but not required Ability