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.

ANGEL is seeking a highly motivated Research Scientist in the area of green computing, with a focus on computing and networking system energy efficiency optimization. The postdoc will play a key role in advancing our research agenda on sustainable data canters and energy-aware computing, bridging system-level performance with energy consumption reduction.

Key Responsibilities:

• Conduct research on energy-efficient computing systems, including CPU/GPU/accelerator architectures, virtualization, and workload scheduling;
• Develop models and algorithms for energy-aware scheduling, workload prediction, and performance-energy trade-off optimization;
• Investigate network system energy efficiency, including traffic scheduling, topology optimization, and SDN-based solutions;
• Perform trace-driven evaluation using large-scale cluster datasets (e.g., Google cluster data, Alibaba traces);
• Contribute to the design of green cloud and edge computing architectures;
• Publish results in top-tier conferences and journals, and collaborate with industrial and academic partners.

Requirements:

• PhD in Computer Science, Computer Engineering, Electrical & Electronic Engineering, or related disciplines;
• Strong research background in systems, networking, or distributed computing, with demonstrated focus on energy efficiency;
• Experience in workload modeling, power/performance optimization, and energy-aware scheduling;
• Knowledge of optimization methods (e.g., convex optimization, combinatorial optimization, reinforcement learning) for energy management;
• Familiarity with simulation and modeling tools (e.g., gem5, NS3, CloudSim, GreenCloud);
• Experience with large-scale workload traces and trace-driven performance evaluation;
• Proficiency in Python, C++ or Java for system-level experimentation;
• Strong publication record in relevant venues (e.g., SIGMETRICS, ASPLOS, OSDI, INFOCOM, IEEE TPDS);
• Cross-disciplinary research experience integrating computing/IT systems with energy system modelling is a plus;
• Industrial or collaborative project experience in cloud/edge systems, high-performance computing, or energy-efficient networks is desirable;
• Fluent spoken and written English for research work.

We regret that only shortlisted candidates will be notified.