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.
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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