Knowledge Graph Scientist, ARTC

Roles and Responsibilities

• Design and govern the core ontology covering parts, processes, regulations, and lifecycle states in EV battery, aerospace, and manufacturing domains, ensuring methodology scalability regardless of the industry domain
• Develop and evaluate multi-hop reasoning methods, rule-based inference, and neuro-symbolic approaches that enable the system to derive new knowledge from existing graph facts
• Research and prototype GraphRAG pipelines and LLM-to-KG extraction methods that automatically populate the graph from technical documents, inspection reports, and sensor logs
• Develop cross-domain ontology alignment methods to unify terminology and schemas across data sources (e.g. engineering and supply chain), including handling conflicting industry standards
• Research temporal knowledge graph structures that model how part conditions and states evolve across the remanufacturing lifecycle, enabling historical reasoning and predictive inference
• Lead and contribute to research proposal development, technical roadmapping, and capability-building initiatives that align with industry needs and organisational strategy; proactively identify emerging technology opportunities and define new research directions for the KG programme.
• Collaborate with the KG Engineer to operationalise research prototypes into production-grade graph systems
• Prepare technical documentation, research reports, and presentations for internal and external stakeholders
• Publish findings at relevant research venues or domain-specific manufacturing and sustainability conferences

Job Requirements

Technical Skills

• Proficiency in Python, with experience in graph database systems (e.g., Neo4j, or Stardog)
• Strong foundation in knowledge representation and reasoning, including ontology design (OWL, RDFS, SKOS) and graph query languages (SPARQL, Cypher)
• Deep understanding of knowledge graph embedding methods (e.g., TransE, RotatE, ComplEx) and link prediction techniques
• Experience with LLM integration for knowledge tasks: Retrieval-Augmented Generation (RAG), information extraction, or fine-tuning language models on technical text
• Familiarity with neuro-symbolic AI approaches combining neural learning with logic-based reasoning
• Proficiency in one or more of the following areas:Temporal knowledge graphs and evolving graph structuresMulti-hop reasoning and path-based inferenceNamed entity recognition and relation extraction from technical documentsKnowledge graph completion and missing link prediction

Bonus Skills (Preferred but not required)

• Familiarity with industrial or manufacturing data formats (BOMs, maintenance records, inspection schemas, ISO standards)
• Experience with digital twin frameworks or manufacturing informatics platforms

Qualifications

• PhD in Computer Science, Artificial Intelligence, or a related engineering field, with a focus on knowledge representation, semantic web, or graph-based machine learning
• Preferred: Demonstrated track record of independently securing competitive research grants, funding awards, or industry-sponsored projects.
• Preferred: At least 2 years of relevant post-doctoral or industry research experience, preferably with exposure to industrial or manufacturing knowledge domains

The above eligibility criteria are not exhaustive. A*STAR may include additional selection criteria based on its prevailing recruitment policies. These policies may be amended from time to time without notice. We regret that only shortlisted candidates will be notified.