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familiarity with ML frameworks such as PyTorch, Jax, or TensorFlow. A strong foundation in statistical methods, probability theory, or uncertainty quantification is highly advantageous. Job Family Postdoctoral
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analytical models, datasets, and risk monitoring tools in collaboration with DOE national laboratories and federal partners. Prepare detailed reports and briefings on methodologies, analyses, and findings
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deposition (ALD). The project involves performing quantum mechanical calculations (e.g., first principles density functional theory (DFT)) to identify the structures and to understand the complex mechanisms
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: Experience with generative models (transformers, diffusion models, VAEs, GANs) applied to biological sequences Familiarity with the theory behind modern molecular biology techniques Knowledge of HPC
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apply analytical models and datasets in collaboration with DOE national laboratories and federal partners. Prepare detailed reports and briefings on methodologies, analyses, and findings. Collaborate with
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(microelectromechanical systems) devices for X-ray optics at synchrotron radiation sources. Some background of the project is given in the publications listed below. The idea is to make highly nonlinear MEMS-based
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Knowledge, Skills, and Experience: Proficiency in mathematical analysis and operator theory. Experience working with microelectronics. Experience in conducting synchrotron experiments and analyzing
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physics, nuclear theory, nuclear data, accelerator R&D and operates the DOE Office of Nuclear Physics National User Facility, the Argonne Tandem-Linac Accelerator System (ATLAS). As an equal employment
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The Materials Science Division at Argonne National Laboratory seeks to make a postdoctoral appointment for research in the area of thin films of quantum materials for microelectronics. The work will
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materials. Additional expertise in machine-learning methods, programming, robotics is highly desirable. Job Family Postdoctoral Job Profile Postdoctoral Appointee Worker Type Long-Term (Fixed Term) Time Type