This is a 12-month internship position in Intel's Emerging Systems Lab. The candidate will be contributing to Intel's research in the Probabilistic Methods in Machine Learning. The candidate will work with the team on developing innovative algorithms to apply probabilistic methods to problems in image/video processing and computer vision. The work will further involve implementing the developed algorithms efficiently on Intel platforms, and working with business units to integrate the solutions into Intel's product offerings. The candidate will also have opportunities to publish their work in top-tier academic journals and conferences. Must possess the below qualifications to be considered for this position. Experience listed below would be obtained through a combination of your school work/classes/research and/or relevant previous job and/or internship experiences.

Qualifications

� Candidate must be pursuing a M.S./M.Tech./Ph.D. degree in Electrical Engineering, Computer Engineering or a related field. � Knowledge of machine learning and AI algorithms including deep-learning � Coursework in probability theory, machine learning, linear algebra � Familiarity with deep-learning frameworks such as Tensorflow and Pytorch � Coursework in probability and statistics � Knowledge or experience with one or more of the following is a plus: image processing, video processing, image/video compression, computer vision.

Inside this Business Group

Intel Labs is the company's world-class, industry leading research organization, responsible for driving Intel's technology pipeline and creating new opportunities. The mission of Intel Labs is to deliver breakthrough technologies to fuel Intel's growth. This includes identifying and exploring compelling new technologies and high risk opportunities ahead of business unit investment and demonstrating first-to-market technologies and innovative new usages for computing technology. Intel Labs engages the leading thinkers in academia and industry in addition to partnering closely with Intel business units.

Work Model for this Role

This role will be eligible for our hybrid work model which allows employees to split their time between working on-site at their assigned Intel site and off-site.

Legal Disclaimer:

Intel prohibits discrimination based on race, color, religion, gender, national origin, age, disability, veteran status, marital status, pregnancy, gender expression or identity, sexual orientation or any other legally protected status.

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