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implementations for agentic AI, LLM deployment, and automated laboratory integrations (e.g., self-driving labs). Facility Support & Training: Dedicate a portion of time to general ALCF user support, including
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in GPU programming one or more parallel computing models, including SYCL, CUDA, HIP, or OpenMP Experience with scientific computing and software development on HPC systems Ability to conduct
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their publications Experience programming GPUs with CUDA, SYCL, HIP or OpenMP Experience using and developing code with AMReX Experience in performance engineering to improve code scalability and reduce time-to
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. This position is part of the DOE-BES initiative Integrated Scientific Agentic AI for Catalysis (ISAAC), a multi-facility collaboration integrating experimental measurements, simulations, and data science to
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needs. As a part of this team, you will : Develop, build and test equipment and perform molten salt separations which support the development of a secure fuel supply for MSRs and nuclear fuel cycle
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. As part of a multidisciplinary team spanning multiple Argonne divisions, you will contribute to the design, fabrication, and characterization of superconducting devices based on high kinetic inductance
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. This exciting project focuses on further development of beam position monitoring structures and high gradient testing of components. They will play a key role in designing, fabricating, and testing advanced
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for predicting the reliability of high-temperature structural components. This involves working with various continuum damage mechanics models and statistical reliability models. The goal is to enhance engineering
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equity, and external market pay for comparable jobs. Additionally, comprehensive benefits are part of the total rewards package. Click here to view Argonne employee benefits! As an equal employment
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, asphaltenes, resins, and kerogen molecules and contribute to engineering design of upscaled processes. The candidate will be a part of the Applied Materials Division (AMD) within AET at Argonne and will