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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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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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demonstrates a professional attitude. Skilled written and verbal communicator, including the ability to present complex information so that it is understandable to a broad audience. Strong computer skills
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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
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independent research productivity through publications, software, datasets, or similar outputs Excellent communication skills, the ability to work effectively in interdisciplinary teams Ability to model
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The Center for Nanoscale Materials (CNM) at Argonne National Laboratory seeks an outstanding postdoctoral researcher to advance data-driven, physics-informed AI for microelectronics materials
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diffraction, electron microscopy (SEM and TEM), spectroscopy (FTIR and Raman), and electrochemical (EIS and battery cycler). Publication record with excellent written and oral communication skills. Ability
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circuits that couple to both macroscopic magnetic crystals as magnon target mass and solid-state single-electron qubit with magnetic field compatibility. Develop superconducting hybrid circuits that couple
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for Microelectronics” —a physics-informed AI framework that links composition, structure, and operating conditions to defect evolution and functional performance. The successful candidates will lead experimental
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science, or a closely related field Experience in the development and/or deployment of superconducting devices, or related technologies in quantum devices, low-temperature systems, microelectronics, or