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. machine learning, computer science, mathematics, statistics, physics, theoretical neuroscience or a closely related field) with significant post-qualification research experience. You will experience
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Background in machine learning or deep learning methods, including Graph Neural Network (GNN) Experience with Large Language Models (LLMs) applied to biological data collection, extraction, and standardization
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Learning: Apply shape-correspondence and machine-learning approaches, including spectral/graph-based surface alignment and autoencoder-based shape extraction, to compare limb and joint morphology across
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, please visit: http://uhr.rutgers.edu/benefits/benefits-overview . Posting Summary Rutgers, The State University of New Jersey, is seeking a highly qualified candidate for a Postdoctoral Research Associate
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. The postdoctoral researcher(s) will join an international research environment at Umeå University, including Stat4Reg (www.stat4reg.se ), which develops statistical and machine-learning methods for register data
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Curious about opportunities for learning, creating, discovering, and innovating, and are encouraged to learn from failure. Show Your Fire by joining our team and exhibiting your passion and pride in your
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– Wrocław University of Science and Technology offers employees the opportunity to teach students and participate in training programmes. International cooperation – Wrocław University of Science and
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-cell or spatial transcriptomic analysis is desirable. Experience with machine learning, LLMs, AI agents, multimodal data integration, or automated biological interpretation workflows is desirable but not
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. The project will involve drone optimizations, computer vision, closed-loop control, multi-modal sensor fusion, machine learning, and transfer learning methods. The candidate will also work in collaboration with
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | about 17 hours ago
. Candidates with expertise in related areas, such as lattice field theory algorithms, machine-learning methods for lattice field theory, hadron spectroscopy, or hadron-structure phenomenology, are encouraged