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Field
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-Geometric Foundations of Deep Learning or Computer Vision KTH Royal Institute of Technology, School of Engineering Sciences Job description The Department of Mathematics at KTH welcomes applications for a
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and leadership in key academic areas. Learn more at https://www.uta.edu/administration/president/strategic-plan/rise100 . This is an exciting time to join UTA and contribute to its bold vision
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | about 2 months ago
models (LLMs), Computer Vision and and Deep Learning architectures (CNNs, RNNs, etc). * Strong foundational knowledge in machine learning and data science methodologies. * Proficiency in programming, with
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neuroethology and robotics. This position focuses primarily on the neuroethology aspects of our research program, with particular emphasis on understanding the behavioral and neural mechanisms underlying active
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MACQUARIE UNIVERSITY - SYDNEY AUSTRALIA | North Ryde, New South Wales | Australia | about 1 month ago
years. This role will develop and apply new machine-learning based approaches for extremely precise radial velocity studies and exoplanet spectroscopy with the NEID, HPF, and MARVEL facilities, and pursue
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imaging, computer vision, and predictive modelling. The postdoc will further develop an existing rumen‑fill scoring algorithm into a functional prototype and pilot the technology for longitudinal monitoring
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competitive ERC. The project focuses on the development of a first-principles, machine-learning-accelerated computational framework for modelling polymorphism, anharmonicity, and electron–phonon interactions in
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, computer vision and machine learning algorithms. · Information dissemination and decision-support services · Policy related analysis and investigation · Previous interactions with transportation funding
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that preserves object identity or style. They should have a solid publication record in top-tier computer vision conferences such as CVPR, ICCV, or ECCV, and demonstrate proficiency in deep learning frameworks
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. This involves the development of mathematical models for signal transmission/reception, derivation of performance limits, algorithmic-level system design and performance evaluation via computer simulations and/or