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Physics with applications to Material discovery and quantum chemistry - Gen AI designing materials with target properties Astrophysics and cosmology: Deep learning for telescope image analysis
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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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Information regarding postdoctoral fellow salary, which is determined by the number of years post PhD, can be found at https://postdoc.hms.harvard.edu/guidelines Minimum Number of References Required Maximum
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, mathematical logic or statistical learning theory. For PhD position 2, we appreciate prior experience in implementing deep learning models for graphs and networks. Our offer As a PhD candidate at UT, you will be
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) Position Description: Apply Position Description Faculty, National Math Camps (Summer Role) National Math Camps is seeking passionate and energetic math faculty to help us inspire and teach mathematically
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learning, power systems, and IoT and gain hands-on experience through simulations, hardware-in-the-loop (HIL) testing, and prototypes development. We offer: 4-year PhD position in one of the largest, most
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George Emil Palade University of Medicine, Pharmacy, Science, and Technology of Târgu Mureș | Romania | 4 days ago
). Label-driven / weakly-supervised CNNs for multimodal deformable registration (arXiv / MICCAI threads) — key papers showing deep learning approaches for fast, deformable registration. https://doi.org
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: Research: Evidence of sustained research impact and experience supervising student research Technical Proficiency with: Deployment of machine learning and deep learning models Modern JavaScript/TypeScript
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specifically, do you want to perform cutting-edge research and develop novel advances in hyperbolic deep learning for computer vision? Then check out the vacancy below and apply for a PhD position in this
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candidate will undertake tasks in WP2 on the AI4MAP project focusing on developing deep learning models for ecological perception and characterisation of Posidonia oceanica seagrass. The tasks include