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Ref.: 532117 Work type: Full-time Department: Teaching and Learning Innovation Centre (47500) Categories: Senior Research Staff & Post-doctoral Fellow Applications are invited for appointment as
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methodology, theory, and applications across the areas of Bayesian experimental design, active learning, probabilistic deep learning, and related topics. The £1.23M project is funded by the UKRI Horizon
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development workshops for teachers and school leaders on STEAM education, artificial intelligence, science education, language education, self-directed learning and innovation leadership; coordinate and liaise
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on the physical layer and MAC layer design for wireless systems, particularly across multiple spectrum bands. The PDA is expected to actively disseminate results through publications in high-impact journals and
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design of optical wireless communication systems as enablers of wireless access/backhaul of 6G and beyond networks. The PDA will be jointly supervised by Murat Uysal and Qurrat-Ul-Ain Nadeem. Principal
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to determine areas of enhanced predictability Work closely with collaborators to design the machine-learning based forecasting tool to be used by Climate Prediction Center forecasters Visit the Climate
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University of New Hampshire – Main Campus | New Boston, New Hampshire | United States | about 3 hours ago
Beam Physics Case: This project involves performing realistic simulations to support the physics case for incorporating a polarized deuteron beam in the EIC. Duties/Responsibilities Design and execute
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Information Position Summary The University at Buffalo School of Management (SOM) is an official awardee of the New York Semiconductor Manufacturing and Research Technology Innovation Corridor Tech Hub
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interested in computational materials design and discovery. The successful candidate will develop new, openly accessible datasets and machine learning models for modeling redox-active solid-state materials
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. The primary objective is to design robust and efficient planning solutions—integrated within a digital twin—that account for the uncertainties and variability inherent in industrial processes. Machine learning