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Argonne National Laboratory Postdoctoral Appointee - Computational Statistics, Applied Probability, Machine Learning in Lemont, Illinois

The Mathematics and Computer Science division at Argonne is seeking a postdoctoral candidate with a strong background in computational statistics and uncertainty quantification to develop robust and effective algorithms for solving stochastic inverse problems in quantum chromodynamics and nuclear tomography. The incumbent will be part of a large team of applied mathematicians, statisticians, computational scientists, and nuclear physicists. The work involves sample-based probability distribution learning and spans computational statistics, machine learning, uncertainty quantification, and numerical analysis.

Position Requirements

  • Recent or soon-to-be completed PhD (typically completed within the last 0-3 years) in statistics, probability, applied mathematics, or another scientific or engineering field with a strong emphasis on solving inverse problems and uncertainty quantification.

  • Effective verbal and written communication skills

  • Mixture of experience and interest in learning the following topics: Inference-based computational statistics: Bayesian & likelihood methods, high dimensional sampling, computational and variational inverse problems, dimension reduction and statistical analysis of high-dimensional datasets

Desired but not required knowledge:

  • Statistical testing, goodness-of-fit tests, probability coverage

  • Machine learning (ML) with a strong emphasis on variational, probabilistic and statistical ML approaches, normalizing flows, generative modeling

  • Optimal transport, distance metrics

  • Python code development, and devops such as git and CI/CD

Job Family

Postdoctoral Family

Job Profile

Postdoctoral Appointee

Worker Type

Long-Term (Fixed Term)

Time Type

Full time

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