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Position: Online Course Developer – RHIN XXX Clinical Data Science and Machine Learning Location: Remote (U.S.-based only) Division: Rabb School of Continuing Studies, Brandeis University
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Artificial intelligence and machine learning methods for model discovery in the social sciences School of Electrical and Electronic Engineering PhD Research Project Self Funded Prof Robin Purshouse
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Professorial Research Fellow in Artificial Intelligence (AI) and Machine Learning (ML) to provide leadership and vision in advancing research excellence. This is a unique opportunity to apply cutting-edge
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University of the Arts London, London College of Communication; | London, England | United Kingdom | 18 days ago
The opportunity Join University of the Arts London (UAL) and play a vital role in shaping, supporting and coordinating Digital Learning activities within London College of Communication (LCC) and
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their concerns. Responsible for developing and maintaining partnerships throughout the College. Coordinates and facilitates interactive workshops and engagement opportunities that promote experiential learning
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The High Energy Physics Division at Argonne National Laboratory invites applications for a postdoctoral research associate position to conduct research in machine learning (ML) for applications in
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looking for postdoctoral researchers in the area of computer vision, AI, and machine learning. The initial appointment will be for 2 years with a possible extension with a tentative start date in January
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of the ERC Consolidator project AUTOMATIX (see details below), we are seeking a PhD candidate to develop machine learning approaches for constitutive modeling. Context With the advent of machine-learning (ML
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Introduction to Machine Learning Staff TA - College of Engineering - Integrated Innovation Institute
the disciplines of engineering, design, and business, we can build impactful solutions that create value for real people. Carnegie Mellon University's Introduction to Machine Learning course is looking for a
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knowledge and advanced transfer learning techniques. The methodology incorporates fundamental radar wave propagation equations into the diffusion process, allowing for more accurate and physically consistent