Xerox Research Intern, Hybrid Physics-ML Reasoning for Diagnostics and Prognostics in North Carolina
Research Intern, Hybrid Physics-ML Reasoning for Diagnostics and Prognostics
City: Palo Alto, Cary
State/Province: California, North Carolina
Country: United States
Department: Student Programs
Date: Tuesday, April 6, 2021
Working time: Full-time
Job Level: Individual Contributor
Job Type: Internship
Job Field: Student Programs
Description & Requirements
PARC, a Xerox company, is in the Business of Breakthroughs®. Practicing open innovation, we provide custom R&D services, technology, expertise, best practices, and intellectual property to Fortune 500 and Global 1000 companies, startups, and government agencies and partners. We create new business options, accelerate time to market, augment internal capabilities, and reduce risk for our clients. Since its inception, PARC has pioneered many technology platforms – from the Ethernet and laser printing to the GUI and ubiquitous computing – and has enabled the creation of many industries. Incorporated as an independent, wholly owned subsidiary of Xerox in 2002, PARC today continues the research that enables breakthroughs for our clients' businesses.
PARC's Intelligent Systems Laboratory has an internship opening in the area of hybrid reasoning that combines physics modeling and machine learning towards diagnostics and prognostics of industrial equipment. The work will involve fundamental research on determining how to best leverage physics understanding of components and systems with machine learning approaches to overcome challenges of both training data scarcity and overhead of physics modeling. The work will explore different types of architectures to determine which approach will likely yield best performance.. This is a fundamental research project for which publications and patents are highly encouraged.
The ideal candidate would be a Ph.D. student in mechanical engineering, electrical engineering, computer science, applied physics, or a related field, with the skills described below.
Deep understanding of 1 st principles physics modeling.
Deep understanding of the working of neural networks, particularly deep learning algorithms
Experience with ML pipelines, particularly the use of pytorch, tensorflow, keras, pandas, numpy.
Ability to set up and evaluate classifiers using python-based ML pipelines.
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