About the job:
We are excited to extend to you an opportunity for a job as an AI/ML expert to join our dynamic AI research team. You will have the chance to conduct cutting-edge research in Intelligent Operations and Management of future Autonomous Network Data Management systems.
Responsibilities:
- Contributing to the development of new approaches to automate data management using generative AI, models, reinforcement learning, and machine learning theory.
- Proposing and developing advanced AI/ML models to address real-world data management and governance issues.
- Experience with foundation models (i.e. Generative Models, LLM) for Goal-based planning and Utility-based optimisation is a great plus.
- Experience with agent-based autonomous systems (i.e. Multi-Agent Systems) is a great plus.
- Translating mathematical problem definitions and model/solution specifications into efficient executable code
- Conducting experiments on large-scale data sets to verify and evaluate the proposed ideas and solutions
- Keeping track of the latest progress in the (selected) AI/ML areas: generative AI, generative models, diffusion models, reinforcement learning, generative agents, large language models, optimisation and machine learning theory
- Contribute to rapid and iterative development of validated software prototypes based on AI technologies, and benchmark alternative solutions
- Proposing high-impact intellectual properties (e.g., patents); publishing or contributing to the publishing of research papers in top-tier AI/ML venues
- Working closely with the team, as well as other Huawei units (if needed)
Requirements:
- A Ph.D. degree in Computer Science, Communications, Statistics, Applied Mathematics, or a related field; or a master's degree.
- 2+ years of industrial or post-doctoral research experience in Artificial Intelligence or Machine Learning, with a focus on Reinforcement Learning (RL) and Deep Learning (DL) technologies.
- Experience with foundation models (i.e. Generative Models, LLM) for Goal-based planning and Utility-based optimisation is a must.
- Strong practical experience with Machine Learning (i.e., Neural Networks, Support Vector Machines, Decision Trees, Ensemble models, Clustering, Association relationship mining, Bayesian Networks).
- Hands-on experience with building, hyper-parameter fine-tuning, and incrementally training neural networks for time-series and graph-structured data.
- Experience with data-centric AI methods (i.e. Drift Detection, Data Augmentation, Data Balancing, Outlier Removal) for systematic engineering of training data prior to model induction is a great plus.
- Experience with agent-based autonomous systems (i.e. Multi-Agent Systems) is a great plus.
- Knowledge of data storage, processing, and quality management technologies in a plus
- Award-winning experience in international competitions is an asset
- Publications in high-quality AI/ML venues (e.g., NeurIPS, ICML, ICLR, AAAI)
- Good communication skills, willingness to collaborate, self-motivated
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