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related field. Experience with finite element simulations and developing constitutive models. Knowledge of high temperature creep crack growth. Knowledge of engineering design codes such as the ASME Boiler
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: Proficiency in machine learning, statistical modeling, and quantitative methods for multi-omics data analysis Molecular Simulations: Expertise with molecular simulation tools like OpenMM, AMBER, Gromacs, and
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readout circuit designs informed by device characterization and electromagnetic simulations Contribute to the development of complete instrument systems for characterization of solid-state single-photon
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to quantify energy consumption, performance and cost benefits. In this role, a successful candidate will perform vehicle modelling and simulation of advanced powertrains to quantify the impacts of new component
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architectures and device technologies Developing and applying simulation and modeling tools for detector performance, characterization, and validation Providing technical feedback to guide intelligent on-detector
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the development of AI models for robotic control and the demonstration of these methods via simulation and experiment. Beyond the listed projects, the candidate may contribute to other large-team scientific
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extreme regional compound flooding within a global Earth system model. The aim is to enable realistic coastal flooding simulations within seasonal-to-decadal simulations and to advance DOE’s capability
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running simulations or AI workflows on supercomputers Experience with training or applying large language models for research Experience with MPI and Input/Output (I/O), and data management Experience in
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modeling of x-ray spectroscopies sensitive to molecular chirality; simulations of x-ray–induced ultrafast electron-transfer, decay, and nuclear dynamics in gas- and liquid-phase systems; and the development
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devices, nonlinear optics, or microwave photonics Working knowledge of simulation tools such as COMSOL or Lumerical for electro-optic modeling Desirable Skills Proficiency in Python-based data analysis