Postdoctoral Fellow Position

(Posted June 15, 2021)

A position for Postdoctoral Fellow is available at the University of Michigan, Mechanical Engineering Department, UQ-SciML Group. The candidate will develop theory and algorithms for Bayesian methods of uncertainty quantification (UQ) and machine learning (ML). Research involves, for example: variational inference, Markov chain Monte Carlo, information divergence estimation, batch and sequential optimal experimental design, reinforcement learning (RL) and inverse RL for sequential Bayesian experimental design, and Bayesian and physics-informed neural networks. These UQ/ML methods will be developed and implemented alongside applications in engineering and science, such as fluid mechanics, material physics, and physically-based biomedicine. Thus, the candidate will work in a collaborative and interdisciplinary environment that intersects statistics, ML, computational science, and engineering physics.

To apply:

Please send your CV, representative publications, and contact information for three references to Prof. Xun Huan ( Please also include a cover letter describing your specific interest in the position, and skills and experience that relate to this position. Review of applications will begin immediately.

Required Qualifications:

  • PhD in a field of applied mathematics, statistics, computer science, or related engineering or science subject area
  • Technical expertise in UQ and/or ML
  • Technical expertise in at least one of: Bayesian inference, optimal experimental design, variational inference, or Monte Carlo methods
  • Publication record indicative of relevant research expertise
  • Excellent interpersonal, written and oral communication skills

Desired Qualifications:

  • The use of state-of-the-art UQ/ML tools in the physical sciences
  • Knowledge and expertise in Python, MATLAB, R, Julia, C, C++, or related languages
  • Experience in high-performance, distributed, or parallel computing
  • Knowledge and expertise in computational science and software development
  • Expertise in Bayesian neural networks, or in reinforcement/inverse reinforcement learning
  • Ability to work in collaborative, interdisciplinary research environments on problems comprising diverse application domains
  • A background in solving practical problems in science and engineering that involve encounters with real-world data

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