217 machine-learning-"https:"-"https:"-"https:" Fellowship positions in United Kingdom
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- York St John University
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in mathematical modelling and Bayesian inference while learning from three collaborating chief investigators. You will also build your publication record and professional networks through seminars
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learning platforms including LinkedIn Learning Progressive, considerate leave provisions to empower your work-life balance and well-being, including leading parental leave, gender affirmation leave and
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Inverse Methods and Ionospheric Modelling Research Fellow - School of Engineering - 106995 - Grade 7
to completion) relevant to empirical modelling (any discipline), machine learning (any discipline), inverse methods (any discipline), ionospheric modelling and/or ionospheric measurement techniques, radio
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, accumulated deformations, and their impact on structural performance, particularly for compression members. Develop data-driven reusability assessment platforms integrating NDT data, machine learning models
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About the Role An exciting opportunity has arisen for an Application Engineer to join the Power Electronics, Machines and Control Institute (PEMC) at the University of Nottingham. This is a hands
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As a University of Applied Learning, SIT works closely with industry in our research pursuits. Our research staff will have the opportunity to be equipped with applied research skills
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, relevant experience in computer-based statistical analysis and presentation of results, demonstrated proficiency in a coding language used for data analysis, such as Python or R, proof of previous data
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a focus on Physical Activity and Health related content. You may also teach more broadly exercise physiology, sport and exercise psychology, and/or research methods. You will have a PhD in a related
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, or experimental testing of plasma devices within vacuum chambers, and a demonstrated aptitude for learning new fields of research. The person should have a PhD, or equivalent qualifications and experience, in
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systems to rapidly traverse evolutionary landscapes Evolution of complex, multi-gene phenotypes Engineering plug-and-play selection systems for continuous evolution of diverse phenotypes Learn more at How