Researcher – AI Scientific Algorithm Discovery
Job in
Montreal, Montréal, Province de Québec, Canada
Listed on 2026-06-09
Listing for:
Huawei Technologies Canada Co., Ltd.
Contract
position Listed on 2026-06-09
Job specializations:
-
Research/Development
Artificial Intelligence, Data Scientist -
IT/Tech
Artificial Intelligence, Data Scientist, AI Engineer (Applied/Software)
Job Description & How to Apply Below
Location: Montreal
Huawei Canada has an immediate 12-month contract opening for a 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:
Design and develop AI‑driven systems for scientific algorithm discovery, including models that can propose, test, and iteratively improve algorithms.
Research autonomous scientific discovery systems, enabling self‑evolving algorithmic frameworks that adapt based on experimental feedback.
Collaborate with interdisciplinary teams to apply AI‑discovered algorithms to real‑world scientific challenges.
Publish research findings in top‑tier conferences and journals, and contribute to open‑source or internal research platforms.
About the ideal candidate:
A PhD or Master’s in computer science, machine learning, applied mathematics, or a related field.
Demonstrated ability to conduct independent research and system development.
A strong publication record in top‑tier AI conferences (e.g., NeurIPS, ICML, ICLR, ACL).
Proven research track record with publications or impactful projects in relevant areas.
Strong background in machine learning, optimization, and scientific computing.
Experiences with algorithm self‑evolving methods or autonomous scientific discovery are preferred.
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