Xerox Research Intern, R&D, Computer Science & Engineering in Palo Alto, California
Research Intern, R&D, Computer Science & Engineering
City: Palo Alto
Country: United States
Department: Research & Development
Date: Thursday, May 13, 2021
Working time: Full-time
Job Level: Individual Contributor
Job Type: Internship
Job Field: Research & Development
Seniority Level: Associate
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.
The Intelligent Systems Lab at PARC has need for an intern with experience in formal methods, satisfiability and Satisfiability Modulo Theory (SMT). The area has multiple ongoing projects that contain combinatorial optimization components. The internship will match the intern with the project that best matches their abilities. A successful internship will result in papers at major AI conferences and patent applications.
Currently pursuing a MS or PhD degree in Computer Science, Applied Mathematics, Information and Data Science, or related fields.
Successfully completed beginning-graduate coursework in AI.
Proven record of scientific investigation as demonstrated by project reports, conference presentations, publications (e.g., AAAI, IJCAI)
Experience in programming in Python or LISP and in software engineering.
Experience in empirical evaluation for complex intelligent agents.
Experience with combinatorial optimization, satisfiability, machine learning or algorithmics.
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