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/functional inequalities Markov processes and stochastic analysis Theoretical analysis of neural networks and deep learning Foundations of reinforcement learning and bandit algorithms Mathematical and
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qualifications include: Experience with radio interferometric observing, data processing, and imaging. Experience with modern machine learning / deep learning techniques and software packages. Experience with time
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: 276187783 Position: Postdoctoral Research Associate Description: Ecology and Evolutionary BiologyPostdoctoral Research AssociateThe Department of Ecology and Evolutionary Biology at Princeton University has a
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., NeurIPS, ICML, ACL, EMNLP, etc.). Proficiency in programming languages such as Python, and experience with deep learning frameworks like TensorFlow, PyTorch, or JAX. In-depth understanding of transformer
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pathway prediction. Apply deep learning techniques to predict reaction outcomes, optimize reaction conditions, and identify novel synthetic routes. Curate and manage reaction datasets from literature
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Free probability theory High-dimensional probability, concentration and functional inequalities Mathematical aspects of machine learning and deep neural networks Free Probability aspects of Quantum
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: Statistical Physics / Statistical Physics Appl Deadline: 2026/02/28 11:59PM * (posted 2025/06/29, listed until 2026/02/28) Position Description: Apply Position Description A 1 year postdoctoral position is
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An exciting postdoctoral position is available in the exciting field of mathematics of deep learning, under the joint supervision of Prof. Alex Cloninger and Prof. Gal Mishne at UC San Diego. This NSF-funded
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Postdoctoral Researcher in Multimodal Machine Learning for Precision Cancer Medicine The Machine Learning in Biomedicine (MLBioMed) research group at the Institute for Molecular Medicine Finland
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Electrical Engineering, Computer Science, or a related field Strong background in speech processing, signal processing or machine learning Proficiency in Python and deep learning frameworks Experience with far