439 machine-learning "https:" "https:" "https:" "https:" "https:" "https:" "https:" "U.S" "U.S" "U.S" Fellowship positions in United Kingdom
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transfer, thermo-fluid systems simulation and programming, as well as exposure to software platforms and algorithms for Artificial Intelligence, Machine Learning and Data Analytics. You will have experience
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analysing qualitative data, synthesising findings, and supporting the preparation of reports, presentations, and academic publications. This role will be based at the Centre for Appearance Research (CAR
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Machine Learning (OxCSML) research group, with responsibility for leading and carrying out research pertinent to the project, as well as day-to-day management of research activities relevant to the project
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delivering high-quality teaching and learning, and research and innovation with a campus-wide focus on providing a sector-leading student experience. A culturally rich university with a palpable commitment to
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-minded institution with a relentless focus on delivering high-quality teaching and learning, and research and innovation with a campus-wide focus on providing a sector-leading student experience. A
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responsibilities will include: Explore innovative methods for food process optimization including the use of AI and machine-learning Develop and execute methods for characterizing and linking the texture and
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information/data from one structure can be used to manage similar structures from the same population. New machine learning, sensing and digital twin technologies will be developed with the aim of driving new
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Computer Science, Artificial Intelligence, Software Engineering, or a related field. Strong programming proficiency in Python and/or C++. Demonstrable experience with machine learning frameworks (e.g., PyTorch
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, computer vision, and predictive modelling, alongside excellent IT, analytical, interpersonal and communication skills in spoken and written English. You must be able to collect, process and present data with
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application of innovative Machine Learning (ML) frameworks to understand and predict the global hydrological cycle. The role will require bridging the gap between process-based physical modeling and scalable