Job Description

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 Information Systems Laboratory has an internship opening in the area of adversarial machine learning. The work will involve fundamental research on attacking and stress-testing computer vision systems. The intern will work with PARC researchers to formulate and analyze attacks on ML algorithms used in computer vision systems for object and scene classification. This is a fundamental research project for which publications and patents are highly encouraged.

The ideal candidate would be a Ph.D. student in electrical engineering, computer science, or a related field, with the skills described below.

Skills desired:

1. Deep understanding of the working of neural networks, particularly deep learning algorithms and Generative Adversarial Networks (GANs). Particular experience with deep-learning-based object detection and recognition (FRCNN, YOLO, RetinaNet) and image/video classification based on state-of-the-art network architectures (ResNet, AlexNet, VGGNet, Inception) is highly desired.
2. Experience with ML pipelines, particularly the use of pytorch, tensorflow, keras, pandas, numpy.
3. Ability to set up and evaluate classifiers using python-based ML pipelines.
4. Scripting experience in bash/zsh and experience with using github/gitlab.
5. Experience with cloud-based or GPU-based implementations for fast execution of ML pipelines is not required but would be highly desired.

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