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The Nuclear Technologies and National Security Directorate (NTNS) is seeking a dynamic and passionate Postdoctoral Appointee with strong background in statistics or machine learning to lead an
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Publications: 1. P. Chen et al., Ultrafast photonic micro-systems to manipulate hard X-rays at 300 picoseconds, Nat Commun, 10:1158 (2019). https://doi.org/10.1038/s41467-019-09077-1. 2. P. Chen et al., Optics
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energy supply systems, multi-objective and stochastic optimization, advanced statistical analysis, and data visualization. This position offers the opportunity to work with a multidisciplinary team of
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-throughput laboratory systems Proficiency in Python or similar programming languages for data processing, statistical analysis, and integration with AI/ML tools Excellent written and verbal communication
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experimental biologists Assay Expertise: Functional understanding of quantitative and high-throughput assays, particularly in biological signaling and screening contexts Machine Learning & Statistics
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., Python, Fortran, C++) Knowledge of data analysis techniques and statistical methods Proven scientific writing and oral communication skills Ability to work both independently and collaboratively in a team
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for predicting the reliability of high-temperature structural components. This involves working with various continuum damage mechanics models and statistical reliability models. The goal is to enhance engineering
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cells and electrolyzers is welcomed. Experience with statistical analysis methods such as PLS-DA, supervised learning and database building are highly encouraged. The applicant is expected to think and
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simulations on the Aurora supercomputer, using AMReX (https://amrex-codes.github.io/amrex/ ) and the lattice Boltzmann method (LBM). The candidate will develop flow/geometry-aware refinement strategies that go