32 machine-learning-"https:"-"https:"-"https:"-"https:"-"https:"-"U.S" PhD positions at University of Nottingham in United Kingdom
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using multimodal approaches including advanced imaging, nano-mechanical characterisation and machine learning techniques Developing physics-informed reliability models using experimental datasets
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physics or computational data analysis (Python/R/MATLAB, machine learning, or bioinformatics) is highly desirable. Interested candidates should send a CV to michael.chappell@nottingham.ac.uk . Applications
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into the sample of interest. Recently we have been using AI and machine learning to predict the distortion present and significantly speed up this correction process. This PhD project will take the latest in AI
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of the Manufacturing Technology Centre (MTC) and academics within the Power Electronics, Machines and Control (PEMC) Research Institute , University of Nottingham. The project will be supported by the state-of-the-art
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CFD, thermofluids and machine learning. Experience in Python (or another language), machine learning frameworks, or CFD tools such as OpenFOAM is beneficial but not required. Applicants should hold (or
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, particularly MRI, medical physics or computational data analysis (Python/R/MATLAB, machine learning, or bioinformatics) is highly desirable. Interested candidates should send a CV to michael.chappell
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, Machines and Control (PEMC) Research Institute , University of Nottingham. The project will be supported by the state-of-the-art electric motor manufacturing platforms at both locations. Project Description
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This exciting opportunity is based within the Power Electronics and Machines Control Research Institute at Faculty of Engineering which conducts cutting edge research into power electronics
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of the Manufacturing Technology Centre (MTC) and academics within the Power Electronics, Machines and Control (PEMC) Research Institute , University of Nottingham. The project will be supported by the state-of-the-art
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computer science, mechanical engineering, or aerospace engineering. You should have programming experience applied to physics/engineering problems and/or experience with machine learning and ML. The University