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mechanics, finite element modeling, and scientific machine learning. The RSE will contribute to the design, implementation, and maintenance of open-source software libraries that integrate phenomenological
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seminars, MA seminars and/or specialist classes. The selected candidate is expected to teach courses on topics in the field of quantitative finance, machine learning and data science. Courses should be
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Scene Understanding Detection and Identification of Objects (SSUDIO) project. The purpose of this project is to develop scene understanding from 3D scans of ships by applying machine learning/computer
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. Experience in coding (e.g., Python/R/Matlab) and experience in behavioural experimentation, statistics, or machine learning is desirable but full training will be provided. Interviews for this studentship
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accordance with FAIR principles, combined with skills in statistical analysis, machine learning and/or data science. Experience with programming languages such as R, Python, or similar will be considered
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into actionable insights, novel tools, and impactful research outcomes. Key Responsibilities Develop, implement, and optimise AI/ML models (artificial intelligence/classical machine learning, deep learning
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. Ability to learn and apply complex policies quickly, including admissions, financial aid, and military education procedures. Demonstrated ability to build positive professional relationships with diverse
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: Monday – Friday, 8 a.m. – 5 p.m. Summary The Michael E. DeBakey Department of Surgery is seeking a Research Associate to implement and maintain the ATLAS (Applied sTatistics and machine Learning
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of visualisation, machine learning, and human-computer interaction under the joint supervision of both institutions. The position is shared by TU Wien and USTP and offers the opportunity to conduct research at both
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | about 9 hours ago
(SHRA) Position Title UTS - Temporary Computer Technician- Inspection and Customer Support at UNC Chapel Hill Position Number Vacancy ID S026725 Full-time/Part-time Full-Time Temporary Hours per week Work