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Master Thesis Intern - Machine Learning

Electronic Arts
Stockholm Stockholm County Sweden
4 years ago
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About SEED
SEED is a cross-disciplinary team within EA Worldwide Studios. Our mission is to explore, build and help define the future of interactive entertainment and we strive to the be the innovators and leaders of the gaming industry. We are found in many locations (including Los Angeles and Montreal) and our headquarter is in Stockholm next to DICE. We have currently a group dedicated to ML and how it can be used for games and game creation.

Background
In gaming industry test automation is one of the key pillars to ensure to deliver quality in coming titles. Graphical glitches makes games feel/look undone resulting in poorer experience for the players. EA ships dozens of titles in millions of copies every year thus every improvement in the process can leverage significantly the impact on game creation. To improve quality and to reduce turnaround time we want to automate the process of finding graphical anomalies. We are looking for a diploma student which can help us achieving this goal. This is a unique opportunity to learn more about ML and how it can be applied to creation of games.

Research problem
Use Machine Learning (ML) to identify so called graphical glitches. It includes, but not limits to: stretched textures, texture seaming, and textures clipping. It’s a demanding problem but image classification is one field where ML really excels and often is super-human in performance. The hypothesis is that if enough training data is provided it should be possible classifying anomalies that up until now is only detected by a trained human professional. At EA we have access to abundant and annotated data from playtests were graphical glitches were found. The data is in it current form unstructured but easily available within our systems.

Project information
You will be closely working with one of the few teams in the world that makes research within the field of AAA gaming. You are expected to be able to work independently but will receive supervision support and access to world class gaming knowledge, computational resources and data.

Duration: The thesis project is expected to start in January 2020.

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