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applications in beamline science and high energy physics, as well as interaction with device and materials researchers. Primary responsibilities will be to design and implement new methods to deploy, monitor
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basic and applied research in the field of metallization and metal production, such as molten salt electrolysis, metallothermic reduction, including physico-chemical property determination. The candidate
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fluids and multi-component vaporization dynamics applied to sustainable aviation fuels (SAF). Work as a part of a multidisciplinary team involving experimentalists, CFD experts, and computational
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, primarily for recycling used nuclear fuel for use in advanced reactors. As a part of this team, you will: Apply electrochemical engineering principles to develop processes such as metal oxide reduction and
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) methods. The materials and devices would consist of patterned multilayer magnetic thin films, ferroelectric materials such as Zr-doped HfO2, as well as novel quantum materials. Of particular interest is the
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that integrate simulation, machine learning, and data analysis. Numerical optimization methods (e.g. machine learning including deep neural networks, reinforcement learning, data mining, genetic algorithms
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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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and Python) Knowledge of data analytics and statistical methods Demonstrated strong scientific writing skills and oral communication Ability to work both independently and collaboratively as a team
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, and spatial transcriptomics. Key responsibilities include: Developing AI/ML methods for image alignment across modalities Automated feature detection Predictive modeling of vascularization patterns
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-based high-resolution surface-sensitive methods. In addition, working with the APS staff scientists, the successful candidate will demonstrate the applications to probe surface and interface nanomaterials