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Massachusetts Institute of Technology | Cambridge, Massachusetts | United States | about 5 hours ago
science, or a related field; experience with using and building machine learning models, developing and validating computational analysis workflows, and developing circuit models is preferred; excellent
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Union within the project “A Comprehensive Trustworthy Framework for Connected Machine Learning and Secure Interconnected AI Solutions (CoEvolution)”, - CUP F23C24000210006 – selection code: ipd_10D_0426_09/IINF
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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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data analysis is required. The lab mostly uses R for data analyses; knowledge of R is not required, and the postdoctoral scholar will have the opportunity for mentorship and learning. To Apply: Motivated
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candidate will have recently completed (or be close to completing) a PhD in Computer Science, Machine Learning, Natural Language Processing (NLP), or a related field, with a thesis focused on AI, specifically
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for physical 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
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at other institutions. The International Union, United Automobile, Aerospace and Agricultural Implement Workers of America (UAW) is the exclusive representative of the postdoctoral scholars. https
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. Description: The Postdoctoral Fellow will work with a team of world-class collaborators on the JWST Cycle 1 GO Program 2512 (https://www.stsci.edu/jwst/science-execution/program-information.html?id=2512
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machine learning methods as well as in biological data analysis are needed for the position. The postdoctoral researcher will play a leading role in this research, including methods development, data
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approaches Applying statistical modeling, causal inference, and machine learning approaches to identify determinants of developmental robustness Applying causal inference approaches to identify critical