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We are seeking a highly motivated Postdoctoral Appointee with a strong background AI/ML specifically in the development and application of Large Language Models (LLMs) tailored for scientific use
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The Hydrogen and Fuel Cell Materials Group in Argonne National Laboratory’s Chemical Sciences and Engineering Division is seeking to hire a Postdoctoral Appointee to participate in a project that
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microscope, as well as electrostatic beam blanker or ultrafast pulser in electron microscopes. Proficient in data analysis and modeling, with experience using Python and other programming or simulation tools
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position to develop and apply advanced analysis methods, including artificial intelligence and machine learning algorithms and approaches, for x-ray science and instruments. These methods will accelerate
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the Interfacial Processes Group (Chemical Sciences and Engineering Division), will use a combination of synchrotron X-ray based tools (e.g., X-ray reflectivity, tomographic imaging, x-ray diffraction, spectroscopy
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the performance and scalability of large-scale molecular dynamics simulations (e.g. LAMMPS) using machine-learned potentials (e.g. MACE) through algorithmic improvements, code parallelization, performance analysis
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The Advanced Grid Modeling group at Argonne National Laboratory's Center for Energy, Environmental, and Economic Systems Analysis is seeking a dedicated Postdoctoral Researcher. This role is ideal
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, applied to combustors for aerospace propulsion systems. The work will take advantage of both commercial and in-house codes and leverage high-performance computing (HPC). Perform high-fidelity nozzle-flow
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-aware multi-modal deep learning (DL) methods. At Argonne, we are developing physics-aware DL models for scientific data analysis, autonomous experiments and instrument tuning. By incorporating prior
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data analysis implemented in languages such as python or Matlab Desirable: Experience with dilution refrigerators and closed-cycle cryogenic systems. Experience with microwave engineering and various