35 linked-data-"https:"-"https:"-"https:"-"https:"-"https:"-"https:" "Open Polytechnic" Postdoctoral positions at Argonne
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availability. Relevant Publications: 1. P. Chen et al., Ultrafast photonic micro-systems to manipulate hard X-rays at 300 picoseconds, Nat Commun, 10:1158 (2019). https://doi.org/10.1038/s41467-019-09077-1. 2
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solver, capable of running simulations on the Aurora supercomputer, using AMReX ( https://amrex-codes.github.io/amrex/ ) and the lattice Boltzmann method (LBM). The candidate will develop flow/geometry
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/CD, software testing, benchmarking, or open-source software development. Familiarity with biomedical AI validation, data readiness assessment, model evaluation, regulatory-grade evidence generation, or
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closing this gap. The successful candidate will work in the Data Science and Learning division of the Computing, Environment, and Life Sciences directorate of Argonne National Laboratories. Primary
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(within the last 0-5 years) in field of Physics, Astronomy, or Quantum Engineering Considerable analytical skills are necessary to develop and improve techniques for data analysis Demonstrated
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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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; assisting with data analysis and technical documentation; and contributing to publications, reports, and sponsor deliverables. The successful candidate will work closely with staff researchers and
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thin films and heterointerfaces. The postdoc will lead experimental design, data acquisition, and quantitative reconstruction. The appointees will work within a highly collaborative team spanning
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. The successful candidate will develop a novel multiscale multimodal experimental apparatus with precise control during data acquisition, as well as work on data processing pipeline, adaptable to imaging
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electropolishing, flash polishing, and focused ion beam methods. Experience applying computer vision, image analysis, and/or machine-learning methods to microscopy or materials characterization data