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# Knowledge Graph and Ontology Specialist

- **Company:** University of Sheffield
- **Location:** Sheffield, UK
- **Experience:** Experienced
- **Contract:** Temporary contract
- **Skills:** Artificial Intelligence, Graph Database, Interoperability
- **Published:** September 10, 2026
- **Apply:** https://www.jobs.ac.uk/job/DSW402/knowledge-graph-and-ontology-specialist

## About the Role

As a specialist, you will design and develop semantic architectures to make industrial data understandable, interoperable, and queryable. Ideally with 2-3 years of experience in knowledge engineering or conceptual modelling, you will establish information pipelines to extract semantic structure from information sources and apply advanced conceptual frameworks (including 3D vs. 4D approaches) to accurately capture complex engineering lifecycles.

## Description

Are you an experienced knowledge engineer who enjoys solving complex data challenges? We have an exciting opportunity for you to join us as a Knowledge Graph and Ontology Specialist and build the semantic foundations for the future of industrial data.

You will join the AMRC at the University of Sheffield, as part of a growing interoperability team currently funded through the leadership of a UKRI Future Leadership Fellow. We are tackling critical barriers of system interoperability preventing organisations from leveraging the benefits of leading-edge technology, such as digital twins and AI, by unifying the current siloed infrastructures to drive industrial adoption. While the wider project focuses on accelerating industrial interoperability approaches, this role is dedicated entirely to the knowledge graph engineering and ontological understanding underpinning its success.

As a specialist, you will design and develop semantic architectures to make industrial data understandable, interoperable, and queryable. Ideally with 2-3 years of experience in knowledge engineering or conceptual modelling, you will establish information pipelines to extract semantic structure from information sources and apply advanced conceptual frameworks (including 3D vs. 4D approaches) to accurately capture complex engineering lifecycles.


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