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and modeling of properties and spectra of PFAS chemicals with the goals of improving detection of PFAS compounds, replacing PFAS in plasma etching processes, or identifying solid adsorbent additives
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a track record in computational modelling that explores the dynamics of AI systems and the development of autonomous AI agents, experience with machine learning, reinforcement learning, and generative
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work that underpins the scientific research of the collaboration. Research Title: Process Modeling using Physically Informed Machine Learning The work will entail: § Designing and training physics
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: The Infrastructure Materials Group at the National Institute of Standards and Technology seeks a researcher with knowledge of reliability testing and modeling for advanced semiconductor packaging and extensive
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-generation seismic design and post-earthquake evaluation procedures. Key Responsibilities: - Synthesize knowledge from past experimental tests and high-fidelity models of structural steel components
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simulation models of existing test structure geometries to enable simulation and optimization for specific PIC platforms Investigate methods to optimize test structure designs for more robust determination of
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models, frameworks, and methods to confront complexity underlying persistent societal challenges affecting the well-being of people and the planet. This position represents a unique opportunity to
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sensors will be designed, modeled, fabricated, and fully packaged for deployment. Sensor performance will be measured in the lab and new techniques will be developed to characterize performance for inertial
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to 100 GHz, using custom and commercial test fixtures. Develop and simulate computational models of material-loaded microelectronic devices to quantify electric, acoustic, and piezoelectric fields for a