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: Harvard John A. Paulson School of Engineering and Applied Sciences Position Description: A postdoctoral position is available in the Geometric Machine Learning Group at Harvard University, led by Prof
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on studying the principles of neural computation through recurrent neural networks, dynamical systems theory, and machine learning. - Develop mathematical and computational models of neural networks - Analyze
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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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Massachusetts Institute of Technology (MIT) | Cambridge, Massachusetts | United States | about 1 month ago
data analysis methods to study biological memory circuits and their applications to machine learning. Building on recent work from the Fiete Lab, the role focuses on identifying principles of biological
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AI and physics‑based simulation for complex mechanical systems. We are now seeking a postdoctoral researcher for a project aiming at physics‑informed autonomous control of machines operating in rough
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, and human-centred computing. You will join the Vision & Human-Robot Interaction (VHR) Lab, a multidisciplinary research group working at the intersection of robotics, machine learning, and human
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self-driven, highly motivated, creative with excellent communication skills in written and spoken English and Cantonese. Expertise and knowledge in AI deep learning model development on histology whole
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Associate Research Scientist / Post-Doctoral Associate in the Division of Science (Computer Science)
machine learning. The successful applicant will participate in research involving human computation, knowledge discovery, machine learning, and data science. The position will provide the opportunity
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background in earthquake monitoring. The successful candidate will lead and contribute to developing a machine-learning powered earthquake monitoring and early warning system. The role involves both
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technology (PAT) adds cost, time, and introduction of human error. This project aims to develop a novel, label-free microbial detection method, coupled with machine learning tailored for cleaning validation