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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | 29 days ago
modeling (such as Molecular Dynamics, virtual screening, free energy calculations and Deep Learning), successful experimental collaboration experiences and excellent communication skills are preferred
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | 28 days ago
screening, free energy calculations and Deep Learning), successful experimental collaboration experiences and excellent communication skills are preferred. Special Physical/Mental Requirements Special
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | 17 days ago
Dynamics, virtual screening, free energy calculations and Deep Learning), successful experimental collaboration experiences and excellent communication skills are preferred. Special Physical/Mental
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Computing Applications group in CSD has an immediate opening for a Postdoctoral Research Associate to design, develop, and deploy machine-learning and high-performance computing workflows, algorithms, and
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closely related quantitative discipline. Demonstrated experience with large-scale deep learning models and modern ML frameworks (e.g., PyTorch, JAX, Transformers), including training, fine-tuning
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and obsessive-compulsive disorders), and to optimise neuromodulation therapies such as deep brain stimulation. The team combines intracranial recordings and EEG, brain imaging, brain stimulation
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. You will be responsible of synthesizing results into compelling figures, making and delivering oral presentations, writing manuscripts, and mentoring students. You will be expected to learn basic R
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, continual, active, federated, online, and reinforcement learning. 【Keywords】Machine Learning, Deep Learning, Bayesian Machine Learning, Statistics, Optimization,Reinforcement Learning, Quantization, Low
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studies to identify targets for medical intervention and to generate insight that meaningfully impacts patient care. This position provides direct mentorship from faculty with deep expertise in neuroimaging
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, tape-out, and testing, preferably with applications to AI systems ● Design, analysis, and modeling of AI hardware such as deep neural network accelerators or neuromorphic computing. ● Emerging AI