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complex materials systems. Advanced data analysis and scientific model development using Python or other scientific programming languages, including experience with automation, instrumentation control
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Modeling. Machine Learning Interatomic Potential (MLIP) accelerated simulations. Demonstrated ability of coding in Fortran, Shell, or Python with development experiences. Deep knowledge in excited states and
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to analysis of experimental data. Of particular interest will be new approaches for tackling multimodal data, quantifying uncertainty, providing rigorous theoretical guarantees, and modelling complex physics
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decision-making to improve the safety and reliability of transportation and grid simulations, with contributions to model development, software maintenance, and real-world deployment. You Will: Develop fine
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python. Experience with machine-learning, such as training large language models. Required Application Materials: CV Cover Letter (1 page) Publication List Additional information: Application date
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types (inorganic solids, polymers, glasses, etc). Desired skills/knowledge: MongoDB databases. Prior development of Model Context Protocol (MCP) frameworks. For consideration, please apply with
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for data analysis and predictive modeling. The fellow will join a small, world-class, multi-institutional team advancing microelectronics research through AI-enhanced methodologies. You will: Perform soft X
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in one or more of: Python, C++, Fortran, Julia. Hands-on experience building and training AI models with frameworks such as TensorFlow or PyTorch. Ability to succeed in an interdisciplinary team and
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test novel superconducting device components compatible with extensible quantum processors Analytical and numerical modelling of microwave quantum circuits and signals Design and conduct state-of-the-art