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operating experimental systems for separation processes; conducting laboratory-scale studies involving radiotracers and actinide/lanthanide separations; supporting the development and optimization of aqueous
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The Chemical and Fuel Cycle Technologies Division is seeking a Postdoctoral Appointee to conduct research in chemical process engineering, separations, and process intensification for recovery
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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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language. Working knowledge of UNIX or Linux. Preferred Knowledge, Skills, and Experience Experience with machine learning and accelerator operation. Experience working with complex algorithms. Experience
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together computer scientists, AI researchers, domain scientists, software engineers, and high-performance computing experts. You will help design and implement new methods for multimodal federated learning
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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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expertise in machine learning, computational imaging, computer vision, or signal processing. Proficiency in scientific programming and modern ML frameworks, with the ability to implement and debug research
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learning, or optimization Strong programming skills in Python and experience with scientific computing and machine-learning libraries Ability to work across experimental, robotic, and computational systems
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computing, high-performance computing (HPC), or machine learning (ML) Interest in Standard Model measurements and/or searches for new phenomena Application Review Review of applications will begin once the
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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