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Internal Number: 7071892 Postdoctoral Fellow, Structural Biology Position Information Position Title: Postdoctoral Fellow, Structural Biology Department: Structural Biology Department Posting Link: https
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School graduates over a thousand students who are ready to take on great ambitions and challenges. For more details, please view: https://www.ntu.edu.sg/eee We are looking for a Postdoctoral Fellow to work
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for structural biology. This project sits at the intersection of X-ray scattering and deep learning, aimed at integrating experimental data to predict protein ensemble structures. As an Empire AI-funded fellow
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, scikit-learn, PyTorch, TensorFlow); additional experience with R, MATLAB, or Julia is an advantage. Machine Learning Expertise: Familiarity with causal machine learning, ensemble methods, and deep learning
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, scikit-learn, PyTorch, TensorFlow); additional experience with R, MATLAB, or Julia is an advantage. Machine Learning Expertise: Familiarity with causal machine learning, ensemble methods, and deep learning
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, multimodal, and agentic AI, as well as foundation models, with a focus on geometric deep learning, large-scale knowledge graphs, and large language models. Fellows will also have the opportunity to apply
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, and experience) Benefits: https://www.umaryland.edu/hrs/benefits/ For more information regarding benefits for postdoctoral fellows, please visit the UMB Benefits Summary . Job Summary: Fellows
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: Computational Linguistics, Language Acquisition, Cognitive Modeling, Machine Learning The Department of Linguistics at the University of Michigan invites applications for a one-year Postdoctoral Research Fellow
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: Expertise in machine learning, deep learning, natural language processing and other AI methods in health and life sciences datasets Expertise in advanced computational methods such as network analysis, graph
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. Machine Learning Expertise: Familiarity with causal machine learning, ensemble methods, and deep learning architectures (CNNs, RNNs). Experience with explainable AI (e.g., SHAP, LIME) is preferred