University of Pennsylvania, Mechanical Engineering and Applied Mechanics

Position ID:University of Pennsylvania-Mechanical Engineering and Applied Mechanics-PDA [#11756]
Position Title: Post-doctoral Research Associate
Position Type:Postdoctoral
Position Location:Philadelphia, PA 19104, United States [map]
Subject Areas: Mechanical Engineering
Machine Learning
Applied Mathematics
Appl Deadline:2018/11/15 finished (2018/08/27, finished 2019/05/19)
Position Description:    

*** this position has been closed, and no new applications will be accepted. ***

Position title: Post-doctoral Research Associate

Project description: Probabilistic data fusion and physics-informed machine learning

Background and Description: The Predictive Intelligence Lab is a small dynamic group led by Dr. Paris Perdikaris at the Department of Mechanical Engineering and Applied Mechanics at the University of Pennsylvania. The focus of our research is on developing scalable computational tools for modeling, analysis, and optimization of complex physical, biological and engineering systems. During the course of this project the candidate is expected to work collaboratively with the PI and a team of PhD students to develop probabilistic machine learning algorithms for multi-fidelity data fusion, model discovery, prediction of complex dynamics from incomplete models and incomplete data, and optimization under uncertainty. The developed computational tools aim to benefit a wide range of scientific domains, including applications in materials discovery and optimization, subsurface transport, and earth systems modeling. The initial appointment will be for 1 year, with possible extension up to 3 years subject to satisfactory performance.

Requirements – A Ph.D. or an equivalent degree in applied mathematics, physics, or related engineering disciplines is required. The candidate should have a demonstrated track record of effectively communicating research through journal papers, conference presentations and/or grant proposals.

Desired skills and Experience – Candidates with one or more of the skills below will be preferred: • Strong background in Bayesian inference, machine learning, and scientific computing. • Hands on experience with deep learning frameworks (Tensorflow, PyTorch). • Experience in high-performance computing and simulation of continuum and/or molecular systems. • Demonstrated ability of successfully working in a group.

Applications should be submitted via email ( and should include a cover letter, a CV, a publication list, a detailed summary of research experience and interests, and three references uploaded in one attachment. The ideal start date will be November 1st, 2018, however, flexibility can be accommodated. The posting will remain open until a suitable candidate has been selected.

Application Materials Required:
Submit the following items online at this website to complete your application:
And anything else requested in the position description.

Further Info:
Penn Institute for Computational Science
University of Pennsylvania
3401 Walnut St., Rm 527A
Philadelphia, PA 19104

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