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. The ideal candidate will possess not only a deep conceptual understanding of neuroscience but also advanced technical expertise in machine learning, artificial intelligence, and data modeling approaches. We
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(including neural quantum states), stabilizer and near-Clifford simulation, Gaussian/free-fermion methods, open-system dynamics (Lindblad master equations). - Machine learning for physical systems: deep
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; expertise in several of the following: life cycle assessment, life-cycle cost analysis, pavement simulation, machine learning, deep reinforcement learning, and/or physics-informed modeling frameworks; and
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breeding program. This requires a Ph.D. with excellent knowledge and skills in drones / UAV / UAS data collection, processing, statistical analyses, AI (machine learning, deep learning) and subsequent
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | 7 days ago
world. Position Summary This Postdoctoral Research Associate will conduct advanced research in artificial intelligence, machine learning, computer vision, and medical image analysis. The position will contribute
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | about 11 hours ago
world. Position Summary This Postdoctoral Research Associate will conduct advanced research in artificial intelligence, machine learning, computer vision, and medical image analysis. The position will contribute
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statistical and machine learning, deep learning, chemometrics, multimodal data fusion, computer vision, uncertainty-aware modeling, stochastic control, optimization, and deployable edge-to-cloud decision
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Position Details Position Information Job Title Postdoctoral Scholar Position Number THCAS-DEEP-PDS-FALL26 Vacancy Open to All Candidates Department AAH Earth Environment and Planning Department
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The Applied Mathematics and Computational Research Division at Lawrence Berkeley National Laboratory (Berkeley Lab) is seeking a Postdoctoral Researcher – Scientific Machine Learning & Computational
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Equation, Stochastic simulation algorithms, and approximation methods. ● Experience with single-cell or spatial transcriptomic data analysis. ● Familiarity with machine learning and deep learning