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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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, and spatial transcriptomics. Key responsibilities include: Developing AI/ML methods for image alignment across modalities Automated feature detection Predictive modeling of vascularization patterns
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techniques to enable multimodal online monitoring of chemical and radiochemical separations processes Acquire fundamental data relevant to chemical separations in support of related modeling efforts Analyze
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Applications are invited for post-doctoral positions in the Cosmological Physics and Advanced Computing Group (CPAC) Group in Argonne National Lab. Research at CPAC covers theory, modeling
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-ion battery operation, cathode materials, and degradation mechanisms Excellent written and oral communication skills and the ability to work collaboratively in a team environment Ability to model
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glovebox operation. Demonstrated problem-solving and innovation skills. Organizational and critical thinking skills. Able to prioritize and manage time effectively. Ability to model Argonne’s core values of
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programming. Strong oral and written communication skills. Excellent collaboration and teamwork abilities. Ability to model Argonne’s core values of impact, safety, respect, integrity, and teamwork. Preferred
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independently as well as in collaboration with a multidisciplinary team. Ability to model Argonne’s core values of impact, safety, respect, integrity, and teamwork. Desired: Any prior research experience in
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engineering controls such as gloveboxes or hoods is desired, but not mandatory. Strong interpersonal, written, and oral communication skills. Ability to model Argonne’s Core Values: Impact, Safety, Respect
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with a team. Ability to model Argonne’s core values of impact, safety, respect, integrity, and teamwork. Preferred Knowledge, Skills, and Experience Experience in machine learning/deep learning methods