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Field
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(CHX, 11-ID) beamline at NSLS-II seeks a postdoctoral researcher with a strong background in computational imaging, data science, or scientific computing. This position will focus on developing advanced
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availability of funding and satisfactory performance. Eligibility: Candidates should be US citizens, nationals, or permanent residents, and they must have received a PhD in computational and applied mathematics
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. Job tasks include optimizing intrinsic labeling systems (13CO2, 2H, 57Fe, etc.); developing high-throughput extraction, sample clean-up, and analysis methods for various plant foods and crops; analyzing
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-environment interaction studies. Excellent communication skills and ability to work both independently and in a collaborative, multidisciplinary environment. Experience with high-performance computing and
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Preferred Qualifications: Experience with high-level programming environments, such as Python. Excellent written and oral communication skills. Motivated self-starter with the ability to work independently
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strong interest in and experience with the LHC scientific program This position has a high level of interaction with an international and multicultural scientific community. The Omega Group in the Physics
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-edge high-performance computing (HPC) that incorporate machine learning/artificial intelligence (ML/AI) techniques into visualizations, enhancing the efficiency and reliability of scientific discovery
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Laboratory (ORNL). This position presents a unique opportunity to develop cutting-edge high-performance computing (HPC) and machine learning/artificial intelligence (ML/AI) techniques that incorporate
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to numerical methods for kinetic equations. Mathematical topics of interest include high-dimensional approximation, closure models, machine learning models, hybrid methods, structure preserving methods, and
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on this tradition, the program is now positioned to lead the next era of FLASH and ultra-high dose rate radiotherapy research, leveraging decades of excellence in both biological discovery and technological