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of numerical quantum many-body methods to study model Hamiltonians. Strong background in linear algebra. Preferred Qualifications: Experience with density matrix renormalization group and tensor network
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of scientific computing principles and applications, including numerical methods, simulation techniques, and computational modeling. Proficiency in using version control systems like Git for collaborative
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of interpretability methods to ensure ML outputs are meaningful in scientific contexts. Preferred: Background in biomedical data, healthcare, or AI for life sciences. Experience with parallel computing. Familiarity
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of scientific computing principles and applications, including numerical methods, simulation techniques, and computational modeling. Proficiency in using version control systems like Git for collaborative
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(multiscale, QSP, PBPK, PK-PD).Apply numerical methods, optimization, and parameter estimation to calibrate models to experimental/clinical data.Perform sensitivity and uncertainty analyses to assess robustness
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students to advance project goals. Provide technical guidance and mentoring on CFD, numerical methods, and high-performance computing workflows. 15% - Publication & Dissemination Prepare and submit
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, graduate, and undergraduate students to advance project goals. Provide technical guidance and mentoring on CFD, numerical methods, and high-performance computing workflows. 15% - Publication & Dissemination
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The National Energy Research Scientific Computing Center (NERSC ) at Berkeley Lab seeks a highly motivated Postdoctoral Researcher — Scientific Machine Learning (NESAP) to join the Workflow
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to address computational challenges in different scientific domains. The successful candidate is expected to design, develop, and integrate novel computational techniques, including software and numerical
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for massively parallel computers. Experience with quantum many-body methods. Preferred Qualifications: A strong computational science background. Familiarity with coupled-cluster method. Understanding