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begin in Fall 2026. The following departments/programs have requested to host a postdoctoral faculty member: Anthropology Ethnic Studies Political Science and International Relations Art, Architecture
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Massachusetts Institute of Technology | Cambridge, Massachusetts | United States | about 22 hours ago
orbital dynamics using analytic and numerical methods, (2) planet formation theory, (3) exoplanet architectures and demographics using statistical methods, and (4) planetary interior/atmosphere modeling
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Computing (HPC) system architecture and intelligent storage design. The candidate will contribute to research and development efforts in scalable storage and memory architectures, telemetry-driven system
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research. Prepare and write research papers, presentations, grant proposals. Train Graduate Students. Qualifications: PhD in Computer Science or related field (completed or near completion) Strong
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define requirements and performance specifications for future HEP/NP detector systems Perform detector concept development, system-level design, and optimization leveraging emerging computing architectures
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Qualifications PhD degree in computer science, data science, or related fields. Effective written and verbal communication skills Robust machine learning and natural language processing skills Proficient computer
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on physics analysis and artificial intelligence/machine learning (AI/ML). The successful candidate will contribute to the group’s broad physics program, which includes precision Higgs and Standard
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% to supervising and assisting PhD students. Qualifications • Candidates with a Ph.D. in any area of cognitive neuroscience broadly defined (e.g., Psychology, Neuroscience, Computer Science, or a related field) are
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Confidential Computing and Secure Multi-tenancy. The candidate will be able to make research contributions in areas of system software architectures to support secure computing enclaves on large scale HPC and
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are especially interested in candidates with strong technical expertise in AI architecture design (e.g., Vision Transformers, foundation models, and federated learning), scalable computing on leadership-class