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Massachusetts Institute of Technology | Cambridge, Massachusetts | United States | about 2 months ago
policies, and the impact and incidence of labor market shocks. Will involve detailed and careful analysis and coding of the welfare impacts of government policies, literature reviews, and advanced meta
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the development of meta-optimization techniques that can automatically search for the best algorithm-hardware pair for a given problem. While we have a history of success in optimizing digital
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the AI algorithms state of the art for crater detection. generate an overview of available meta data in coordination with the game developers. identify potential use cases in the science community and
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learning, security, physics, and mathematics. More broadly, Cambridge offers an outstanding research environment across science and technology, with world-leading departments, institutes, and
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closely with investigators in biomedical engineering, neuroscience, and applied mathematics and statistics and provides a unique exposure to both translational human research and clinical neuroscience
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characterizing RF-active meta-surfaces. The ideal candidate would hold a doctoral degree in physical science or engineering and have research experience in materials science, spectroscopy, and electromagnetic
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and conduct cutting-edge research on disease dynamics and epidemiological modeling. The candidate will develop and apply mathematical and computational models to understand the transmission, evolution
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design, open science practices) and data analytic abilities (for example, meta-analysis, simulation studies, multilevel and/or mathematical modeling) In-depth knowledge of (statistical) programming tools
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validation in optical wireless communications, sensing systems, or related areas. Skills Essential: C1 Excellent skills in programming, algorithm development, and mathematical modelling C2 Excellent
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responsibilities The Research Fellow/Senior Research Fellow will: Lead GBD research projects, which may include systematic reviews, meta-analyses, and epidemiological disease modelling. Set research direction and