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(e.g. weather, hydrology). Expertise in data and model parallelisms for distributed training on large GPU-based machines is essential. Candidates with experience using diffusion-based or other generative
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systems. In parallel, they will design and develop agentic AI and physics-aware AI models to accelerate discovery and deepen mechanistic insight in catalysis. This work will be carried out in close
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) experience in code development with parallel programming techniques using the message passing interface (MPI) library Proficiency in writing code with C, C++ and/or Python Ability to demonstrate strong written
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linear programming, mixed-integer programming, nonlinear optimization, stochastic optimization, robust optimization, or multi-objective optimization. Experience integrating machine learning or data-driven
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effects of low-dose ionizing radiation. The position will contribute to the LUCID research program, a multi-institutional effort integrating experimental biology, machine learning, and mechanistic modeling
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four staff members [Ian Cloët, Alessandro Lovato, Anna McCoy, and Yong Zhao] and several postdocs and students. The group has a broad research program in QCD/hadron physics and nuclear structure
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research program at the interface of theory and experiment. Position Requirements Recent or soon-to-be-completed PhD (within the last 0-5 years) in field of physical chemistry, inorganic chemistry
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, electromagnetic interference shielding, and harsh environments. As part of the Materials Engineering Research Facilities (MERF) and the Argonne MXene Innovations (AMI) program, the Nanocomposite Materials and
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, implementing, and troubleshooting complex experimental studies. Proficiency in Python programming for instrument control, data analysis, experimental automation, or autonomous discovery workflows. Strong written
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at the Large Hadron Collider (LHC). The successful candidate will contribute to a broad research program that includes physics analysis, detector performance studies, experiment operations, and upgrade