Postdoctoral Fellow Position

A position for Postdoctoral Research Fellow is available at the University of Michigan, Mechanical Engineering Department, UQ-SciML Group. The candidate will develop theory and algorithms for uncertainty quantification (UQ) and scientific machine learning (SciML). Research involves, for example: computational Bayes, optimal experimental design, variational inference, Markov chain Monte Carlo, information divergence estimation, reinforcement learning (RL), and human decision-making under uncertainty. These UQ/ML methods will be developed alongside applications of complex physical systems, 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:

Interested applicants please send your CV, representative publications, and contact information for three references to Prof. Xun Huan ( Review of applications will begin immediately and will continue until the position is filled.

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
  • 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