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) simulations and reduced order modeling of turbulent and reacting flows relevant to advanced propulsion and power generation systems, such as gas turbines and detonation engines. The successful candidate’s
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simulations, design and conduct experiments, and analyze multimodal data streams in a continuous, real-time loop with minimal human intervention (https://www.nature.com/articles/s41524-024-01423-2 , https
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devices, nonlinear optics, or microwave photonics Working knowledge of simulation tools such as COMSOL or Lumerical for electro-optic modeling Desirable Skills Proficiency in Python-based data analysis
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running simulations or AI workflows on supercomputers Experience with training or applying large language models for research Experience with MPI and Input/Output (I/O), and data management Experience in
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communication skills Ability to model Argonne’s core values of impact, safety, respect, integrity, and teamwork Preferred Qualifications Familiarity with synchrotron-based X-ray techniques and electron microscopy
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to the Lab’s broader effort in CH4 and CO2 utilization R&D. The role will require the individual to work with personnel that perform machine learning and molecular simulations and electrochemical device testing
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simulation, and applications of nuclear science. A primary mission of PHY is to operate the Argonne Tandem Linac Accelerator System (ATLAS) as a national user facility for low-energy nuclear physics. Position
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methodologies and tools for economic and ecological analyses of hydropower systems. The position will involve the development and use of computer models, simulations, algorithms, databases, economic models, and
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candidate will work on cutting-edge research integrating genome-scale language models (GenSLMs) with deep mutational scanning data, and experimental virology to predict viral evolution and identify emerging
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models; 2. Statistical methods, analysis, and inference for large-scale computational simulator applications; 3. Uncertainty representation, quantification and propagation; and 4. Scalable data science