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in AI-modeling, Monte Carlo simulation, and coding. The successful candidate will work alongside a multidisciplinary team, leveraging artificial intelligence and computational methodologies to optimize
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Massachusetts Institute of Technology (MIT) | Cambridge, Massachusetts | United States | about 1 hour ago
and broader hydrogen technologies. Will contribute to establishing FESTIM as a trusted, community-driven tool by designing Multiphysics validation campaigns and cross-code comparison studies. REQUIRED
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progression. The models are informed by a variety of experimental data, utilize different model structures/modeling techniques, are often closed source or coded in proprietary software packages with poor
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the use of MD and QM software tools, best practices for coding and reproducibility, and fundamental concepts in polarizable force fields. Position Requirements This position is primarily on-site, with
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. Preferred backgrounds include nonlinear optics, quantum optics, spectroscopy, or instrumentation. Proficiency in software and coding (e.g., MATLAB, LabVIEW, Python) is expected. Applicants with a strong
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candidate will play a key role in developing and advancing new models and simulations for Computational Fluid Dynamics (CFD) hypersonic codes. Specific tasks include developing new turbulence and transition
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federal, state and Rutgers's compliance standards as they appear in RBHS's Corporate Compliance Policy, Code of Conduct and Interest policy. Acts as a role model for corporate integrity, customer service
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Department Name MICROBIOLOGY Location Code MICROBIOLOGY (06220A) Is this split and/or fully grant funded? Yes Duties and Responsibilities Candidates for the Postdoctoral Fellow or Research Associate position
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Compliance Policy, Code of Conduct and Conflict of Interest Policy. Performs other related duties as assigned. Minimum Education and Experience: Doctoral degree in public health, health communication
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writing C++ and PyTorch. Training and debugging RL agents. Imitation Learning algorithms for robotics or autonomous vehicles. Prior work combining RL with human data or feedback. A track record of code