Skip to content

Researcher - Reinforcement Learning Research Synthesis

    • Edmonton, Alberta
  • 8r866

Job description

Huawei Canada has an immediate 12-month contract opening for a Researcher.

About the team:

The Software-Hardware System Optimization Lab continuously improves the power efficiency and performance of smartphone products through software-hardware systems optimization and architecture innovation. We keep tracking the trends of cutting-edge technologies, building the competitive strength of mobile AI, graphics, multimedia, and software architecture for mobile phone products.

About the job:

  • Continuous Research Surveillance: Actively monitor, identify, and track significant advancements, breakthroughs, and trends in reinforcement learning and related fields (e.g., deep learning, robotics, game theory).

  • Critical Analysis & Synthesis: Deeply analyze research papers, comprehending the theoretical underpinnings, empirical results, and potential limitations of novel algorithms and architectures.

  • Insight Reporting: Author and present clear, concise, and regular insight reports, technical summaries, and literature reviews for both technical and non-technical audiences.

  • Strategic Guidance: Translate research findings into strategic recommendations, highlighting opportunities, risks, and potential new research directions for our internal teams.

  • Knowledge Dissemination: Act as an internal subject matter expert, fostering a culture of learning by sharing findings through presentations, internal wikis, and direct collaboration with research scientists and engineers.

Job requirements

About the ideal candidate:

  • A PhD or MSc in Computer Science, Electrical Engineering, Statistics, or a related field with a focus on Reinforcement Learning or Machine Learning. Demonstrated ability to translate complex theoretical ideas into clean, efficient, and scalable code.

  • Experience in specialized areas of RL such as offline RL, multi-agent RL (MARL), imitation learning, or model-based RL.

  • Proven experience with RL to solve real-world problems.

or