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Center Tutor Department: NE Campus Learn Center Campus Location: Northeast Campus Employee Classification: ISP - Instructional Sup Position Type: PT - Regular Grant Funded: No Compensation Details
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on computational materials science and machine learning applications, with an emphasis on strongly correlated condensed matter systems. This position will be for 3 months Essential Duties and Responsibilities
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Field of study: computer science, mathematics, software design, software engineering, technical computer science or comparable. Machine Learning (ML) models are reaching a maturity level that allows
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About the Opportunity JOB SUMMARY The Sr Machine Learning (ML) Engineer applies expertise in deploying and scaling AI pipelines across at least one major cloud platform (AWS, GCP, or Azure
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/d) in Energy Informatics, specifically for a DFG project in wind power forecasting using machine learning. You are passionate about applying cutting-edge information technology to solve the energy and
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School of Computing & Mathematical Sciences Location: Greenwich and Medway Campuses Salary: £38,249 to £44,128 Contract Type: Permanent Release Date: Friday 25 July 2025 Closing Date: Friday 08 August 2025 Interview Date: To be confirmed Reference: FES013-E We invite applications outlining...
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equipment and the newest technology The chance to independently prepare and work on your tasks An exciting work with personal responsibility in the research field of machine learning for material sciences
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, with a focus on Cyber Security, Machine Learning and/or Data Mining Strong publication records in reputable journals/conferences Excellent programming skills in e.g., Python, Matlab, C++, etc Good
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learning systems, models, and schemes Studying, transforming, and applying state-of-the-art machine learning prototypes to sponsor specific domains Searching and selecting appropriate data sets before
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learning systems, models, and schemes Studying, transforming, and applying state-of-the-art machine learning prototypes to sponsor specific domains Searching and selecting appropriate data sets before