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Polytechnic University (UM6P), Benguerir, Morocco, is seeking for a postdoctoral candidate in the area of machine learning for IoT networks. The candidate must hold (or about to complete) a PhD in the related
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machine learning for practical implementation. Our group studies natural and designs artificial cognitive systems, researches their relationships and develops methods that enable interactions between them
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Intelligence, or a closely related discipline. Strong research background in AI and machine learning, with a focus on efficient or accelerated models. Proven experience with model compression techniques, such as
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value scholarships. About the scholarship The Learning Potential Fund provides support through scholarships and bursaries to students in financial need. The Manoharan Learning Potential Fund Scholarship
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application! Your work assignments We are looking for a PhD student to work on the development of novel spatio-temporal machine learning methods. Our world is inherently spatio-temporal, i.e. physical processes
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Position purpose: The Experiential Learning Coordinator will play a vital role in enhancing the educational experience of law students by coordinating summer externships, in-semester field
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description You will be contributing to developing and implementing novel algorithms at the intersection of computational physics and machine learning for the data-driven discovery of physical models. You will
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psychoactive substances, in seized drug products or clinical samples. The candidate will have the opportunity to work directly with experimentalists to validate predictions made by their machine-learning models
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Dallas County Community College District | Brookhaven, Mississippi | United States | about 7 hours ago
) Position Type Staff Position Summary Responsible for providing support to students, faculty, staff, and community patrons by fulfilling desk functions in all Learning Commons areas. Assists students, faculty
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RAP opportunity at National Institute of Standards and Technology NIST Scientific machine learning methods for trustable accelerated materials characterization and design Location Material