Xerox Senior Research Engineer, AI & Hybrid Modeling in Palo Alto, California
Senior Research Engineer, AI & Hybrid Modeling
City: Palo Alto
Country: United States
Department: Research & Development
Date: Monday, May 3, 2021
Working time: Full-time
Job Level: Individual Contributor
Job Type: Experienced
Job Field: Research & Development
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.
Hybrid Modeling Researcher
PARC is looking for an exceptional candidate who can contribute to the science of physics based modeling and hybrid models. Hybrid models combine traditional engineering modeling methods (e.g., ODEs, DAEs, PDEs), machine learning (e.g., neural networks), and symbolic AI. They are essential for modeling IOT systems for diagnostic and prognostic applications as well as design of cyberphysical components and systems. Future projects include continuing work with various government agencies (e.g., DARPA, DOE, NIST) and Xerox funded AI technology in Digital Design and Manufacturing, IoT, and AI applications.
Research, design, develop, and implement innovative approaches, methods, and algorithms
Work with internal and external collaborators to formulate problems and define requirements
Develop research plans and write proposals
Contribute to projects within PARC utilizing hybrid models for design, diagnosis and control applications
Ph.D. in Mechanical Engineering, Electrical Engineering, Computer Engineering, or related discipline
Demonstrated expertise in physics-based modeling and simulation, machine learning, and optimization (both traditional and AI-inspired), e.g. strong publication record
Demonstrated implementation skills, including expertise in Python and Matlab.
Experience with machine learning platforms (e.g., TensorFlow, Pytorch)
Research mindset and ability to contribute to writing proposals
Self-starting: Can work independently within a multi-disciplinary team
Experience in reading scientific papers and replicating methods
Familiarity with modern software tools and practices
Knowledge of Modelica is a plus
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