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, computational physics, computational materials science, inverse problems, signal processing, x-ray science etc. are encouraged to apply. Position Requirements PhD completed in the past 5 years or soon to be
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contributions to experiments at Fermilab (SeaQuest, SpinQuest) and PSI (MUSE) Detector hardware leadership, including the ALERT time-of-flight detector, the ePIC Barrel Imaging Calorimeter, and the SoLID detector
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field. Hands-on experience with free-space optical alignment, THz beam delivery, electro-optic sampling, polarization optics and imaging, or time-resolved pump-probe experiments. Proficiency in Python
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Mathematics, or a closely related field. Design and optimize multimodal LLMs to encode, fuse, and reason over heterogeneous scientific data from diverse modalities such as numerical tables, text, and images
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monitoring and gradient tests. Participate in training opportunities, including attending the US Particle Accelerator School (USPAS). Position Requirements PhD completed in the past 5 years or soon to complete
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from coal production including coal fines to produce graphite precursor materials. This separation will likely include processes such as froth flotation, gravity separation and leaching. Position
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projects in artificial intelligence, materials engineering, chemistry, and beyond at Argonne National Laboratory. Position Requirements Recently completed PhD within the last 0-5 years in computer science
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Requirements Recent or soon-to-be-completed PhD (within the last 0-5 years) in physics or a closely related field Ability to model Argonne’s core values of impact, safety, respect, integrity, and teamwork
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”, “Firstname_Lastname_cover_letter”. Include links to code examples in your CV (e.g., GitHub page, past project repositories). Position Requirements A recent PhD (completed within 5 years, or soon to be completed) in computer science
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models Disseminate research through publications, presentations, and open-source contribution Position Requirements Recent or soon-to-be-completed PhD (within the last 0-5 years) in Materials Science, Data