112 machine-learning-"https:"-"https:"-"https:"-"https:"-"https:"-"https:"-"U.S" positions at University of Nottingham in United Kingdom
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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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, 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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, 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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We are looking for a researcher, whose expertise lies in machine learning or uncertainty quantification, to work with Professor Richard Wilkinson on an EPSRC-funded project entitled “Scaling Cardiac
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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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well as extensive experience in data analytics/advanced statistics/machine learning is desirable. Research experience in flavour science and/or food chemistry is advantageous. As a people-orientated individual, you
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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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literate, with experience in using Microsoft Office suite applications. High attention to detail Ability to work on own initiative to plan and manage own workload, and work as part of a team #LI-DNI #INT Our
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this information-gathering process. The successful applicant will have strong expertise in programming, and in particular developing AI-based computer vision methods. Ideally, they will have experience
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underpinned by proactive engagement with critical inquiry into the processes of learning in higher education contexts which is evidence-driven in order to enhance student learning and the quality of assessments