The Alibaba-NTU Global e-Sustainability CorpLab (ANGEL) represents a key collaboration between Alibaba Group and Nanyang Technological University (NTU). Supported by the Singapore RIE2025 Fund, ANGEL creates and deploys impactful green digital technologies for global sustainability.

ANGEL’s mission aligns seamlessly with Singapore’s national objectives, such as the Singapore Green Plan 2030 and the Singapore Smart Nation Initiative. ANGEL’s work in developing sustainable and equitable digital solutions and promoting a sustainable and green lifestyle will contribute to a smaller carbon footprint. Its holistic approach will help to secure a brighter, more sustainable future for humanity. 

We are looking for a motivated Research Scientist to conduct innovative research and develop intelligent algorithms and system frameworks that optimize cost, performance, and scalability. The role focuses on leveraging machine learning and reinforcement learning to enhance storage and service efficiency under dynamic workloads, while bridging theoretical research with practical implementation through close collaboration with cross-functional teams.

Job Description:

• Conduct cutting-edge research in cloud computing, edge computing, service computing, and distributed storage systems.

• Design and develop novel algorithms and system frameworks for cost optimization, latency reduction, auto-tiering, caching, and cloud-edge collaborative data management.

• Apply advanced machine learning and reinforcement learning methods to optimize storage and service systems under dynamic workloads.

• Collaborate with researchers and project teams to translate theoretical advances into practical, scalable computing systems.

Job Requirements:

• PhD in Computer Science, Software Engineering, Distributed Systems, Cloud Computing, or a related field.

• Strong research background in service computing, cloud computing, edge computing, and distributed storage systems.

• Solid knowledge of optimization algorithms, system design, and intelligent resource management methods.

• Ability to work independently and effectively in collaborative research environments.

We regret to inform that only shortlisted candidates will be notified.