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The Chemical Sciences and Engineering Division invites applications for a Postdoctoral Appointee to conduct computational research focused on transition metal-containing clusters
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successful candidate will collaborate with scientists in DIS, Environmental Sciences division and Argonne leadership Computing Facility to further develop the AI foundation models, benchmarking and deploy them
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Qualifications: Experience using the MOOSE simulation framework is highly desired. Experience fitting complicated physics-based models against test data, including machine learning and Bayesian optimization
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. Your responsibilities will encompass pioneering novel synthesis doping techniques for "quantum grade" diamond, optimizing surface termination methods, and developing deterministic synthesis of pertinent
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computational models for industrial capacity planning, logistics optimization, material flow analysis, and supply chain analysis. Apply artificial intelligence, machine learning, LLMs, and advanced statistical
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. The candidate will work closely with computational modeling collaborators to validate reactor designs and optimize operating parameters. The candidate will be expected to contribute to report preparation
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nitrogen-vacancy (NV) diamond quantum magnetometry for high-energy physics experiments. The HEP Division performs cutting-edge research leveraging advanced detector development, high-performance computing
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learning, or optimization Strong programming skills in Python and experience with scientific computing and machine-learning libraries Ability to work across experimental, robotic, and computational systems
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and engineers across Argonne, including the Materials Engineering Research Facilities (MERF) and the Argonne MXene Innovations (AMI) program, while collaborating with industrial and academic partners
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ideal for someone who enjoys working at the intersection of data science, machine learning, materials research, and experiment, and who is motivated to translate computational advances into real