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Internship

Research Intern – Multimodal Generative Models for Video Games

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Microsoft

Dec 2

🚀 Off-cycle Internship

Cambridge

AI generated summary

  • The candidate must be currently enrolled in a Bachelors or Masters program in AI, ML, CS, Game Development or a related area, with research experience, strong understanding of deep learning, hands-on implementation skills, effective communication, and the ability to work collaboratively. Preferred qualifications include enrollment in a PhD program, top publication experience, and familiarity with cloud infrastructure.
  • The research intern will collaborate with a team to tackle research challenges in multimodal generative models for video games, conducting experiments, coding, and communicating findings through publications and presentations.

Off-cycle Internship

Research & Development, DataCambridge

Description

  • For our Microsoft Research Cambridge, UK, location, we are seeking highly motivated researcher intern candidates in the area of Gaming and AI. We encourage applications from all candidates with a background in Deep Learning (DL), Reinforcement Learning (RL), or a related field, who are excited to tackle challenges that arise in applications of modern machine learning approaches to video games. Working closely with researchers from Microsoft for the duration of 12 weeks, you will advance the state of the art in this space by contributing to the development of novel insights related to evaluation, models and/or algorithms with potential applications to video games. 
  • This is an exceptional opportunity to contribute to ambitious research while collaborating with a diverse team and expanding your expertise in this exciting research area within a leading industrial research lab. Key research challenges that we are currently tackling include, but are not limited to, evaluation of multimodal generative AI models, generalization, model architectures, predictive (world) models, imitation and reinforcement learning, and scaling to large-scale data and compute. The focus and scope of each internship considers the team’s direction as well as successful candidates’ experience and research interests. 

Requirements

  • Currently enrolled in a Bachelors (BSc) or Masters (required) or PhD (preferred) program in Artificial Intelligence, Machine Learning, Computer Science, Game Development or a related area.
  • Ability to carry out research in at least one of the areas mentioned above, demonstrated by at least one journal or conference publication (or demonstratable equivalent experience).
  • Strong understanding of state-of-the-art deep learning approaches.
  • Hands-on experience in implementing and empirically evaluating deep learning approaches.
  • Effective communication skills and ability to work in a collaborative environment.
  • Preferred/Additional Qualifications:
  • Currently enrolled in a PhD program in Artificial Intelligence, Machine Learning, Computer Science, Game Development or a related area.
  • Ability to carry out research in at least one of the areas mentioned above, demonstrated by at least one journal or conference publication in one of the top publication venues in your research area.
  • Demonstrated ability and/or strong motivation to learn to use cloud infrastructure for experimentation is a plus.

Education requirements

Currently Studying
Bachelors
Masters

Area of Responsibilities

Research & Development
Data

Responsibilities

  • In collaboration with your mentor and a diverse team (for example, including designers, engineers, and researchers), contribute to solving an ambitious research challenge and translate your results into actionable insights that are relevant to potential applications in modern video games.
  • Write code and contribute to shared codebases to test the new approach or hypotheses.
  • Distil the developed insights into effective communications, such as a research paper, prototypes, demos and/or a presentation, to reach internal and external technical and general audiences.

Details

Work type

Full time

Work mode

office

Location

Cambridge