Salary: Research Assistant – £33,951 to £35,608; Research Associate – £36,636 to £46,049. Newcastle University offers excellent benefits, including a generous holiday package, the opportunity to buy additional holidays, great pension schemes, and various health and wellbeing initiatives. Closing Date: 21 May 2026.
The Role
We are excited to launch this new opportunity for a Research Assistant/Associate in Urban Knowledge Modelling to join the School of Engineering at Newcastle University. This position aims to design and implement a continuous, multi‑modal evidence cycle that seamlessly integrates qualitative textual knowledge, quantitative pollution simulation, and real‑world observational data. You will play a key role in bridging the qualitative‑quantitative divide by implementing the Knowledge‑Augmented Generation (KAG) framework.
Your primary focus will be on utilising advanced KAG architectures (i.e., OpenSPG) to synthesise vast amounts of unstructured textual evidence alongside real‑world observational time‑series data from urban sensor networks into a structured, multimodal Urban Air Quality Knowledge Graph. This will involve investigating how the multi‑modal knowledge can be rigorously aligned to mitigate noise and filter spurious correlations; deploying a logical form‑guided hybrid reasoning engine within the KAG framework to automate the translation of qualitative policy hypotheses into machine‑readable parameters for quantitative simulation models; and leveraging emerging technologies such as Time‑Series‑to‑Text (TS2T) generation and automated causal discovery to create a dynamic feedback loop that updates the KAG relationships based on empirical evidence.
We are looking for candidates who have experience in creating formal ontologies for urban domains, maintaining structured knowledge graphs (e.g., Neo4j), and a strong grasp of advanced AI reasoning frameworks, specifically foundation models, large language models (LLMs), and KAG pipelines. Expertise in integrating computational simulation models, utilising KAG’s mutual indexing capabilities, and applying time‑series analysis to observational environmental data is highly sought after.
You will join the Digital Innovation in Construction & Engineering Lab (NU‑DICE Lab: https://research.ncl.ac.uk/kassem/). The lab focuses on digitalisation and transformation of the construction and engineering industries, with key research themes including digital twins for urban environments, data‑centric construction, and decarbonisation through digitalisation.
This full‑time position is available immediately on a fixed‑term basis for up to 15 months in the first instance.
Key Accountabilities
- Design and strategic implementation of a Knowledge‑Augmented Generation (KAG) reasoning engine, ensuring effective integration of textual, simulated, and observational evidence for urban air quality management.
- Manage ongoing development and curation of a dynamic, multi‑modal Urban Air Quality Knowledge Graph, applying schema‑constrained knowledge construction and engaging with domain experts to facilitate human‑in‑the‑loop adjudication.
- Direct the continuous empirical feedback loop, applying time‑series analysis to validate and enrich the knowledge base with observational sensor data.
- Present research progress and outcomes to a Principal Investigator or groups overseeing the project.
- Contribute ideas, including enhancements to the technical or methodological aspects of the project.
- Assess research findings for the need or scope for further investigations.
- Contribute to writing up the research and its dissemination through seminars, conference presentations or publications.
- Present research findings at conferences or through publications in reputable outlets appropriate to the discipline.
- Contribute to grant applications submitted by others and develop your own research objectives and proposals for funding.
Knowledge, Skills and Experience
- Experience designing and constructing semantic data models, ontologies, and knowledge graphs to represent complex information, focusing on urban domains and complex socio‑technical relationships.
- Strong appreciation for the qualitative nuances of urban environmental governance, demonstrating the ability to translate complex human‑centric policies into structured, machine‑readable formats.
- Proficiency working with advanced AI reasoning frameworks, large language models (LLMs), and retrieval‑augmented or knowledge‑augmented architectures to enhance model decision‑making.
- Strong programming skills applied to data science, time‑series analysis, and machine learning, including analysing temporal data derived from complex urban environments and integrating dynamic observational data with structural knowledge bases.
- Ability to communicate complex information clearly to diverse stakeholders across academia, industry, and public governance.
- Experience presenting at conferences and/or publishing high quality research.
Attributes and Behaviour
- Commitment to working positively as a member of a multi‑skilled research team.
- Ability to negotiate and prioritise multiple, competing responsibilities and to work to deadlines.
- Commitment to continued professional development.
- Understanding of good practice in equality, inclusion and diversity.
Qualifications
- PhD in Computer Science, Data Science, Urban Analytics, Environmental Engineering, or a closely related field (Research Associate).
- Near completion of a PhD in Computer Science, Data Science, Urban Analytics, Environmental Engineering, or a closely related field (Research Assistant).
Newcastle University is a global university where everyone is treated with dignity and respect. As a University of Sanctuary, we aim to provide a welcoming place of safety for all, offering opportunities to people fleeing violence and persecution. We are committed to being a fully inclusive university which actively recruits, supports and retains colleagues from all sectors of society. We value diversity and celebrate, support and thrive on the contributions of all our employees and the communities they represent. We are a proud equal opportunities employer and encourage applications from individuals who complement our existing teams. We hold a Gold Athena Swan award and a Race Equality Charter Bronze award, and are a Disability Confident employer offering interview support for disabled applicants who meet the essential criteria for the role. In addition we are a member of the Euraxess initiative supporting researchers in Europe.
Requisition ID: 29351
#J-18808-Ljbffr”, “datePosted”: “2026-05-10”, “hiringOrganization”: { “@type”: “Organization”, “name”: “Newcastle University”, “sameAs”: “https://uk.whatjobs.com/pub_api__cpl__428785845__4861?utm_campaign=publisher&utm_medium=api&utm_source=4861&geoID=328” }, “jobLocation”: { “@type”: “Place”, “address”: { “@type”: “PostalAddress”, “addressLocality”: “Newcastle upon Tyne” } } }Salary: Research Assistant – £33,951 to £35,608; Research Associate – £36,636 to £46,049. Newcastle University offers excellent benefits, including a generous holiday package, the opportunity to buy additional holidays, great pension schemes, and various health and wellbeing initiatives. Closing Date: 21 May 2026.
The Role
We are excited to launch this new opportunity for a Research Assistant/Associate in Urban Knowledge Modelling to join the School of Engineering at Newcastle University. This position aims to design and implement a continuous, multi‑modal evidence cycle that seamlessly integrates qualitative textual knowledge, quantitative pollution simulation, and real‑world observational data. You will play a key role in bridging the qualitative‑quantitative divide by implementing the Knowledge‑Augmented Generation (KAG) framework.
Your primary focus will be on utilising advanced KAG architectures (i.e., OpenSPG) to synthesise vast amounts of unstructured textual evidence alongside real‑world observational time‑series data from urban sensor networks into a structured, multimodal Urban Air Quality Knowledge Graph. This will involve investigating how the multi‑modal knowledge can be rigorously aligned to mitigate noise and filter spurious correlations; deploying a logical form‑guided hybrid reasoning engine within the KAG framework to automate the translation of qualitative policy hypotheses into machine‑readable parameters for quantitative simulation models; and leveraging emerging technologies such as Time‑Series‑to‑Text (TS2T) generation and automated causal discovery to create a dynamic feedback loop that updates the KAG relationships based on empirical evidence.
We are looking for candidates who have experience in creating formal ontologies for urban domains, maintaining structured knowledge graphs (e.g., Neo4j), and a strong grasp of advanced AI reasoning frameworks, specifically foundation models, large language models (LLMs), and KAG pipelines. Expertise in integrating computational simulation models, utilising KAG’s mutual indexing capabilities, and applying time‑series analysis to observational environmental data is highly sought after.
You will join the Digital Innovation in Construction & Engineering Lab (NU‑DICE Lab: https://research.ncl.ac.uk/kassem/). The lab focuses on digitalisation and transformation of the construction and engineering industries, with key research themes including digital twins for urban environments, data‑centric construction, and decarbonisation through digitalisation.
This full‑time position is available immediately on a fixed‑term basis for up to 15 months in the first instance.
Key Accountabilities
- Design and strategic implementation of a Knowledge‑Augmented Generation (KAG) reasoning engine, ensuring effective integration of textual, simulated, and observational evidence for urban air quality management.
- Manage ongoing development and curation of a dynamic, multi‑modal Urban Air Quality Knowledge Graph, applying schema‑constrained knowledge construction and engaging with domain experts to facilitate human‑in‑the‑loop adjudication.
- Direct the continuous empirical feedback loop, applying time‑series analysis to validate and enrich the knowledge base with observational sensor data.
- Present research progress and outcomes to a Principal Investigator or groups overseeing the project.
- Contribute ideas, including enhancements to the technical or methodological aspects of the project.
- Assess research findings for the need or scope for further investigations.
- Contribute to writing up the research and its dissemination through seminars, conference presentations or publications.
- Present research findings at conferences or through publications in reputable outlets appropriate to the discipline.
- Contribute to grant applications submitted by others and develop your own research objectives and proposals for funding.
Knowledge, Skills and Experience
- Experience designing and constructing semantic data models, ontologies, and knowledge graphs to represent complex information, focusing on urban domains and complex socio‑technical relationships.
- Strong appreciation for the qualitative nuances of urban environmental governance, demonstrating the ability to translate complex human‑centric policies into structured, machine‑readable formats.
- Proficiency working with advanced AI reasoning frameworks, large language models (LLMs), and retrieval‑augmented or knowledge‑augmented architectures to enhance model decision‑making.
- Strong programming skills applied to data science, time‑series analysis, and machine learning, including analysing temporal data derived from complex urban environments and integrating dynamic observational data with structural knowledge bases.
- Ability to communicate complex information clearly to diverse stakeholders across academia, industry, and public governance.
- Experience presenting at conferences and/or publishing high quality research.
Attributes and Behaviour
- Commitment to working positively as a member of a multi‑skilled research team.
- Ability to negotiate and prioritise multiple, competing responsibilities and to work to deadlines.
- Commitment to continued professional development.
- Understanding of good practice in equality, inclusion and diversity.
Qualifications
- PhD in Computer Science, Data Science, Urban Analytics, Environmental Engineering, or a closely related field (Research Associate).
- Near completion of a PhD in Computer Science, Data Science, Urban Analytics, Environmental Engineering, or a closely related field (Research Assistant).
Newcastle University is a global university where everyone is treated with dignity and respect. As a University of Sanctuary, we aim to provide a welcoming place of safety for all, offering opportunities to people fleeing violence and persecution. We are committed to being a fully inclusive university which actively recruits, supports and retains colleagues from all sectors of society. We value diversity and celebrate, support and thrive on the contributions of all our employees and the communities they represent. We are a proud equal opportunities employer and encourage applications from individuals who complement our existing teams. We hold a Gold Athena Swan award and a Race Equality Charter Bronze award, and are a Disability Confident employer offering interview support for disabled applicants who meet the essential criteria for the role. In addition we are a member of the Euraxess initiative supporting researchers in Europe.
Requisition ID: 29351
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