Researcher - AI-Driven Optimization for Optical DSP

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

About the Team:

Huawei Canada's Advanced Optical Technology Lab focuses on advanced R&D in high-performance optical communications and networking. Our expert team specializes in transmission algorithms, systems, physics, and optical network management. The lab engages in projects ranging from deep research to developing key product features, actively participating in standards organizations and collaborative research with partners. Our multicultural environment fosters innovation, mentorship, and a passion for learning. If you thrive on solving complex technical challenges, this lab is your ideal place.

About the Job:

• Design, evaluate, and optimize advanced DSP blocks for cutting-edge optical communication systems.

• Apply AI and Machine Learning to design and autonomously optimize DSP algorithms for next-generation optical transceivers, targeting performance beyond traditional methods.

• Develop and integrate DSP modules within a large-scale, high-fidelity simulation platform. Explore and implement AI-driven cross-optimization strategies between DSP blocks to maximize system-level performance.

• Create comprehensive technical documentation and deliver presentations to cross-functional teams and stakeholders.

• Engage in forefront research in optical communications, with a focus on Digital Signal Processing (DSP), Forward Error Correction (FEC), and Artificial Intelligence/Machine Learning (AI/ML).

• Develop and implement AI agent-based strategies for the holistic, cross-optimization of interconnected DSP blocks.

About the ideal candidate:

• Master’s degree in Electrical Engineering, Computer Engineering, or Computer Science; PhD is an asset.

• Expertise in Digital Signal Processing (DSP) techniques; strong foundation in digital communication theory; solid understanding of Forward Error Correction (FEC) coding theory (e.g., BCH, RS, OFEC).

• Proven experience in developing and applying Machine Learning and AI agent frameworks for complex system optimization.

• Familiarity with multi-agent reinforcement learning (MARL) or other distributed AI paradigms for controlling and optimizing interdependent subsystems.

• Knowledge of using Large Language Models (LLMs) for code generation, system modeling, or automating design exploration and analysis workflows.

• Programming proficiency in Python, C++, and MATLAB, with experience in AI/ML frameworks such as PyTorch, TensorFlow, or JAX.

• Experience or knowledge in machine learning (ML) is an asset, particularly as applied to physical layer communication and autonomous system design.

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