Senior Researcher - Embodied AI

Huawei Canada has an immediate permanent opening for a Senior Researcher.

About the team:

Founded in 2012, the Noah’s Ark lab has evolved into a prominent research organization with notable achievements in academia and industry. The lab’s mission focuses on advancing artificial intelligence and related fields to benefit the company and society. Driven by impactful, long-term projects, the aim is to enhance state-of-the-art research while integrating innovations into the company's products and services, including LLMs, RL, NLP, computer vision, AI theory, and Autonomous driving.

About the job:

• Working closely with a team of experienced researchers to solve real world challenges in the domain of Embodied AI.

• Developing state-of-the-art approaches for Embodied AI applications. Directions include, but not limited to, generative AI, representation learning, foundation models, reasoning, planning, data generation and augmentation, reinforcement learning, and low-level control.

• Translating mathematical problem definitions and model/solution specifications into efficient executable code.

• Conducting evaluation and empirical studies using robotic platforms in both simulation and real-world.

• Proposing high-impact intellectual properties (e.g., patents), and publishing or contributing to research papers in top-tier AI venues.

About the ideal candidate:

• PhD degree in Computer Science or related fields or master's degree with comparable experience.

• Prior experience in sequence analysis and generative AI, in particular vision and language.

• Proven research record in AI by having at least one paper as the first author in top tier venues, such as NeurIPS, ICML, ICLR, CVPR, ICCV, ECCV, ICRA.

• Proficiency in Python programming language.

• Experience with one or more mainstream deep learning frameworks e.g. PyTorch, TensorFlow.

• Experience in robotics applications, in particular decision-making, planning and control, or experience with real robot and ROS is an asset.

• Experience with using large-scale datasets and sensor data, or with use of various transformer architectures and diffusion models is an asset.

• Experience with imitation and reinforcement learning algorithms is an asset.