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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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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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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 safe laboratory practices. Position Requirements Recent or soon-to-be-completed PhD (within the last 0-5 years) in the field of Chemistry or a closely related discipline Demonstrated expertise in
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all areas of experimental condensed matter physics will be considered, particular emphasis will be placed on the dynamical studies of 2D materials and their functionalities. This postdoctoral position
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or upcoming year (optional) Experience in one or more of the following areas: experimental data analysis related to hadronic physics, polarized targets or beams, silicon sensors, calorimetry, detector
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The Nanoscience and Technology Division (NST) at Argonne National Laboratory (ANL) is looking for experimental postdoctoral candidate in the area of synthesis of single crystal diamond and their
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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