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camera technology and multiplexed readout systems for quantum information science applications. In this role, you will join a multidisciplinary team spanning several Argonne divisions and contribute
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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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extended yearly. Position Requirements A recent PhD (within 5 years) in computational chemistry, chemistry, materials science, physics, computational science, computer science, engineering, or a related
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, focused ion beam specimen preparation, and computer vision or machine-learning analysis of microscopy datasets. The position requires strong experimental, analytical, written, oral, and interpersonal
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initiatives, providing valuable insights to peer reviewers and program managers alike. Position Requirements Required skills and qualifications: Recently completed PhD within the last 0-5 years in an applicable
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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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well as in industry and at other national laboratories. Position Requirements Recent or soon-to-be-completed PhD (typically completed within the last 0-5 years)in Engineering or similar program. At least 2
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Requirements Recent or soon-to-be-completed PhD (within the last 0-5 years) in chemical engineering, materials science, chemistry, or a closely related field Expertise in crystallization science, including
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to establish structure-property relationships and advance the understanding of emergent quantum phenomena. Position Requirements Recent or soon-to-be-completed PhD (typically completed within the last 0-5 years