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.
Please send your CV, representative publications, and contact information for three references to Prof. Xun Huan (email@example.com). 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.
- 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
- 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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