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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 Research Engineer to design and develop an advanced multi-source knowledge fusion framework that leverages Large Language Models (LLMs) and other AI techniques. The role will focus on constructing, merging, and maintaining domain-specific knowledge graphs to harmonize heterogeneous data, specifically applying these technologies to support research and decision-making on critical environmental issues.
Key Responsibilities:
Knowledge graph construction and maintenance: Build, merge, and maintain scalable, domain-specific knowledge graphs, ensuring they adapt to dynamic data evolution and continuous updates.
Data integration and harmonization: Develop robust pipelines to ingest, clean, and integrate heterogeneous data from diverse and inconsistent sources into a unified graph structure.
Collaborative research: Work closely with a multidisciplinary team of researchers to define data requirements, align technical deliverables with project goals, and apply the knowledge graph framework to solve complex environmental challenges.
Job Requirements:
Bachelor / Master degree in computer science or related field
Familiarity with ESG (Environmental, Social and Governance) regulations and standards
Good written and oral communication skills
Proficiency in Python and cloud computing
Team player and able to develop solutions under strict timelines with other researchers
Entry level candidates are welcome to apply
We regret to inform that only shortlisted candidates will be notified.
Job ID: 145584725