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.