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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
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. Experience applying computer vision, image analysis, and/or machine-learning methods to microscopy or materials characterization data. Demonstrated ability to analyze microstructural data and relate
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objectives (e.g. spin characterization of host materials, silicon carbide synthesis for quantum information science, etc.). Collaboration is a cornerstone of our approach, and as such, the candidate will work
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completed within the last 0-5 years)in chemical engineering, materials science, industrial engineering, or related fields. Knowledge of Python, JavaScript, Microsoft Excel and other computer programming
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-generation diamond membrane quantum sensors to enable precision electromagnetic field mapping in HEP experiments. This position bridges detector R&D, materials science, and quantum information
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at the Large Hadron Collider (LHC). The successful candidate will contribute to a broad research program that includes physics analysis, detector performance studies, experiment operations, and upgrade
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PhD (within the last 0-5 years) in field of materials science, chemistry, chemical engineering, computer science, or a related field Experience operating and troubleshooting laboratory automation
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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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to a collaborative, multidisciplinary research program focused on the chemical recycling of polymers and organometallic catalysis. You will work alongside scientists with diverse expertise to design
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The Center for Nanoscale Materials (CNM) at Argonne National Laboratory is seeking postdoctoral researchers to work on the development of single electron on solid neon qubits. The project aims