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English English PhD Research Fellow in Machine Learning and Distributed Data Processing Apply for this job See advertisement Job description Position as PhD Research Fellow in Machine Learning and
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PhD Research Fellow in ML-assisted reservoir characterization/modelling for CO2 storage (ref 290702)
-build ups in potential multi-site storage licenses. The research will help to suggest best practices for machine learning integration in de-risking CO2 storage sites. We seek a candidate with a strong
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(PDE). Examples of models in the scope of the project include particle models, stochastic PDE and models from fluid dynamics and machine learning. Place of work is the Department of Mathematics, Blindern
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students. Assist in grant proposal applications. Requirements: PhD in Mechanical Engineering, Machine Learning, Artificial Intelligence, Computational Mechanics, Material Science, Industrial Engineering, or
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of marine geophysical survey and machine learning algorithms; Job Requirements: A Bachelor degree in geophysics or equivalent and A PhD degree in geophysics / geomechanics or equivalent from a recognized
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good collaborative skills within a team. You may also have strong theoretical and programming background in AI, machine learning, mathematical modelling including physics-based models, and/or robot
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Massachusetts. Specifically, this fellowship is focused on machine-assisted visualization. We welcome applications from recent PhD graduates who are interested in these or related fields, particularly those who
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the applications of Machine Learning. Dr. Anthony Bellotti is Professor in the Department of Computer Science at UNNC. He received his PhD in machine learning from Royal Holloway, University of London
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The Centre for Machine Learning within the Data Science and Statistics Section of the Department of Mathematics and Computer Science (IMADA) at the University of Southern Denmark invites
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climate change - Computer vision, e.g., colour vision, human colour vision, colour appearance models, etc. - AI technology, e.g. statistical learning, neural network learning, deep and transfer learning