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) as evidenced by their publications High performance computing (HPC) experience in code development with parallel programming techniques using the message passing interface (MPI) library Proficiency
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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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flow and turbulent combustion applications, as well as parallel scientific computing. Knowledge of deep machine learning (using TensorFlow, PyTorch, etc.) and agentic AI for multi-fidelity modeling
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branches of physical sciences, or in math, computer science, and electric engineering who have an interest in accelerator physics will also be considered. Strong programming skills. Proficiency in the
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. Programming expertise in Python or other scientific programming languages is a plus. Excellent oral and written communication skills. Experience with automation, control, and instrument infrastructure. Ability
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the Python programming language and contributions to open-source scientific software Scientific productivity, as demonstrated by publications and conference presentations Effective oral and written
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programming for data analysis and experimental control Excellent written and oral communication skills, demonstrated through publications, presentations, and/or professional recognition Experience with
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performance studies, detector operations, and/or upgrade activities Strong skills in scientific programming, software development, and data analysis Ability to work effectively in a collaborative
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strong programming skills (e.g., Python, Fortran, C++) Knowledge of data analysis techniques and statistical methods Proven scientific writing and oral communication skills Ability to work both
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. Candidates with PhDs in physics, computational science, or related fields with interest in accelerator physics and FELs will also be considered. Programming skills including a familiarity with the