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Research Engineer - Reinforcement Learning
at Huawei Canada
# of openings:
At Huawei, we define human progress by innovations that enrich humanity. We do not view connectivity as a privilege, but a necessity. We believe that the impact of information and communications technology should be measured by how many people can benefit from it.
Huawei is a leading global ICT solutions provider. Through our dedication to customer-centric innovation and strong partnerships, we have established end-to-end capabilities and strengths across the carrier networks, enterprise, consumer, and cloud computing fields. Our products and solutions have been deployed in over 170 countries, serving more than one third of the world's population.
Do something that will matter! If you're looking for a meaningful opportunity with the potential to make true impact on the world and the way we live, work and play...let's talk!
We are looking for a PhD or MSc graduate in Computer Science, Electrical Engineering, or related fields to join our team to work on research and development on a cool autonomous project concerning designing innovative motion planning and control methods for an autonomous robotic application. You will be responsible for conducting research on practical approaches for behavioral planning, motion planning, and motion control using deep learning and reinforcement learning methods to be employed on a physical robotic system. The role involves literature review, writing papers for prestigious conferences and journals, parenting ideas, designing novel reinforcement learning algorithms, algorithm deployment in simulators and physical systems, debugging and test.
- PhD in computer science, electrical engineering or related fields or MSc graduates with at least 2+ years of experience
- Must have software development experience in Python and C++
- Speak object oriented programming
- Everyday work with Linux
- Experience in game developments or simulation design is a plus
- Solid background in machine learning, reinforcement learning, and dynamic programming
- Basic understanding of mobile robot kinematics
- Background in control theory (MPC, LQR, DDP, iLQR) is preferred
- Experience in practical application of a variety of motion planning methods (A*, Probabilistic Roadmap, RRT, Potential Field, Voronoi Roadmap, …)
- Software development experience with at least one of the main stream deep learning tools such as Tensorflow, Keras, PyTorch, or Tensorforce