AI Scientist - Manufacturing Knowledge Management

A*STAR - Agency for Science, Technology and Research; Singapore; On-site; SGD 90,000 - 130,000; Full time

Job summary

ARTC in Singapore seeks an AI Scientist – Manufacturing Knowledge Management to conduct applied research on how AI can understand manufacturing environments and formalize tacit expertise into AI-ready knowledge assets through elicitation, knowledge modelling, and applied AI research.

You will translate captured expertise into structured representations like ontologies and knowledge graphs, collaborating with engineers and domain experts to drive AI-based decision-support in manufacturing.

Qualifications

• PhD in AI, Cognitive Science, Information Science, Industrial Engineering, or related field.
• 1–3 years of applied research experience in knowledge engineering, AI, or a related domain.

Responsibilities

• Translate tacit expertise into structured knowledge representations (ontologies, rules, flows, graphs).
• Elicit expertise from domain experts via structured interviews and workshops.
• Conduct applied research to validate and evolve knowledge assets for decision-support.
• Partner with AI engineers to integrate knowledge structures into agentic applications (LLMs, RAG, graphs).
• Develop reusable frameworks for knowledge capture across manufacturing use cases.
• Publish findings via internal reports and external papers.

Skills

AI

Cognitive science

Information science

Knowledge management

Knowledge representation

Communication with domain experts

Applied research mindset

Manufacturing knowledge

BPMN familiarity

Education

PhD in AI or related field

Tools

Ontologies/RDF/OWL/SPARQL

Knowledge graphs

Job description

About The Role

The Advanced Remanufacturing and Technology Centre (ARTC) is seeking an AI Scientist – Manufacturing Knowledge Management to conduct applied research on how AI can understand manufacturing environments; the people, machines, and processes and represent that knowledge in forms that drive the agentic AI manufacturing application's reasoning and decision-support. The role works directly with engineers and domain experts to formalize tacit expertise knowledge into AI‑ready knowledge assets through elicitation, knowledge modelling, and applied AI research.

Key Responsibilities

• Translate captured expertise knowledge into structured knowledge representations; ontologies, decision rules, process flows, and knowledge graph schemas that power the agentic AI's reasoning over manufacturing context.
• Elicit tacit expertise from manufacturing domain experts through structured interviews, workshops, and process walkthroughs to populate the agentic AI knowledge base.
• Conduct applied research on reasoning rules and inference methods to validate, enrich, and evolve knowledge assets for manufacturing decision‑support.
• Partner with AI engineers to integrate knowledge structures into LLM-, knowledge graph‑ and RAG‑based agentic applications.
• Develop reusable frameworks for knowledge capture across manufacturing use cases (e.g., semiconductor manufacturing, precision engineering, aerospace maintenance).
• Advance applied research on expert‑in‑the‑loop methods for knowledge discovery and evolution in manufacturing.
• Publish findings via internal reports, technical disclosures, and external academic or industry papers.

Required Skills

• Background in AI, cognitive science, information science, or knowledge management.
• Experience with knowledge representation technologies (ontologies, RDF, OWL, SPARQL, or knowledge graphs) is an advantage; adjacent skills considered.
• Ability to communicate and build rapport with non‑technical domain experts.
• Applied‑research mindset; curious, rigorous, and comfortable with ambiguity.
• Familiarity with manufacturing or industrial processes is a plus.
• Familiarity with knowledge capture tools or business process model and notation (BPMN) process modeling is an advantage.

Qualifications

• PhD in AI, Cognitive Science, Information Science, Industrial Engineering, or related field.
• 1–3 years of applied research experience in knowledge engineering, AI, or a related domain.