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focus will be on exploring the utility of artificial intelligence models in the study of the above, studying quantum mechanical generalizations, and uncovering connections to statistical models. Position
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The Environmental Science Division at Argonne National Laboratory is seeking a postdoctoral researcher to join a project aimed at improving model representations of carbon cycling and greenhouse gas
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journal papers and patents. Ability to present research at technical meetings and publish results in peer reviewed journal articles. Ability to model Argonne’s core values of impact, safety, respect
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to solve challenging problems in the microelectronics area. Note: Synthesis of bulk materials, first-principles simulations/modeling, and organic or bio-related areas are not in consideration
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) Proficiency in programming languages such as Python or C++ Experience with AI frameworks like PyTorch or TensorFlow Strong communication skills and ability to work in a team environment Ability to model
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., transient absorption and emission), including laser operation, optical alignment, detector interfacing, and data analysis Excellent written and verbal communication skills Ability to model Argonne’s core
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learning methods and frameworks especially applied to physical science problems. Experience with x-ray data analysis and/or modeling, such as crystallography, diffraction, or spectroscopy data analysis and
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been received within the last 5 years or upcoming year Ability to model Argonne’s core values of impact, safety, respect, integrity, and teamwork. Applicants should have considerable knowledge in one
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models, including training, fine-tuning and inferencing, at scale. It will help us better understand and improve DAOS to meet the needs of AI-driven science applications. We expect the postdoc to help
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, and spatial transcriptomics. Key responsibilities include: Developing AI/ML methods for image alignment across modalities Automated feature detection Predictive modeling of vascularization patterns