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Postdoctoral Appointee - Probabilistic Machine Learning
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- The Mathematics and Computer Science Division at Argonne National Laboratory seeks self-motivated and independent Postdoctoral researcher to develop and apply state-of-the-art probabilistic machine learning techniques for the development of efficient and robust surrogate models for scientific machine learning applications.
- Probabilistic machine/deep learning and, especially, the Bayesian framework provides an exciting avenue to address some of the challenges related to reliability and robustness encountered by their deterministic counterparts.
- The selected candidate will also have the unique opportunity to develop and scale probabilistic machine learning approaches for various DOE scientific domains using these state-of-the-art computing resources.
- Recent or soon-to-be completed Ph. D. (typically completed within 3 years) in Computer Science or Mathematics with strong background in one or more of the following: Statistical machine learning, Bayesian deep learning, probabilistic and differentiable programming, probability and measure theory.
- Evidence of relevant achievements in probabilistic machine/deep learning, deep latent variable models, uncertainty quantification or Bayesian inference algorithms research and development, as demonstrated with technical publications, presentations, or software releases.
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