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bench scale micro-computed tomography and ultrasonic sensing methods to evaluate the state of charge and state of health of iron- and lead-based electrodes. Your research will be complemented by studies
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Responsibilities : Spearhead the research and development of predictive models and strategic tools designed to support decision making in strengthening both domestic and international supply chains, particularly
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collaborative research environment Ability to model Argonne’s core values of impact, safety, respect, integrity, and teamwork Preferred Qualifications Experience with device nanofabrication Experience in
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, and biological signals to construct coherent models of microbial organization across scales. Success in this role will require creativity in computational imaging, machine learning, and signal
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machine learning models at a world-class high-performance computing facility The candidate will have access to state-of-the-art computing resources, including: NVIDIA DGX-2 Systems: Powerful platforms
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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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with internal and external research partners to advance project objectives and meet program milestones Analyze experimental data and prepare reports, technical presentations, and updates for internal
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. The successful candidate will work in the Data Science and Learning division of the Computing, Environment, and Life Sciences directorate of Argonne National Laboratories. Primary responsibilities will be
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fluorescence tomography at beamline 2-ID-E and 2-ID-D with focus on bioimaging of soil aggregates, as well as computational framework for modeling and reconstruction of related 3D datasets. The successful
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platform for X-ray absorption spectroscopy by integrating LLMs, scientific machine learning, physics-aware workflows, and strong computational chemistry/electronic-structure expertise. The researcher will