Research Assistant/Associate in Urban Knowledge Modelling

The Role

We are excited to launch this new opportunity for a Research Assistant/Associate in Urban Knowledge Modelling to join us in the School of Engineering at [Employer hidden — sign up to reveal]. You will join an innovative team focused on pioneering advanced knowledge modelling specifically for urban air quality management and proactive environmental governance.

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 this 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 this 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 to enable proactive, ex-ante policy testing; and leveraging emerging technologies like Time-Series-to-Text (TS2T) generation and automated causal discovery to process sensor data to create a feedback loop that dynamically 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: research.ncl.ac.uk/kassem/). The primary mission of the NU-DICE Lab is to drive the digitalisation and digital transformation of the construction and engineering industries, focusing on process efficiency and transformative innovation. The lab's key research themes include 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. For more information or informal enquiries, please contact Dr Xiang Xie ([Employer hidden — sign up to reveal]).

To apply, please complete an online application and upload a plain text copy of your CV and covering letter. In your covering letter, please outline how you are meet or exceed all the essential requirements for the role holder as outlined in the job description, and highlight any expertise relevant to the position.

Key Accountabilities

• Design and strategic implementation of a Knowledge-Augmented Generation (KAG) reasoning engine, ensuring the effective use of frameworks (e.g., OpenSPG) to integrate textual, simulated, and observational evidence for urban air quality management
• Manage the ongoing development and curation of a dynamic, multi-modal Urban Air Quality Knowledge Graph, holding responsibility for the rigorous application of schema-constrained knowledge construction and engaging with domain experts to facilitate human-in-the-loop adjudication and ensure the scientific fidelity of knowledge
• Direct the continuous empirical feedback loop, taking responsibility for the application of time series analysis to ensure observational sensor data effectively validates and enriches the knowledge base
• Present information on research progress and outcomes to a Principal Investigator or groups overseeing the research project
• Contribute ideas, including enhancements to the technical or methodological aspects of the project
• Assess research findings for the need/scope for further investigations
• Contribute to the writing up of the research and its dissemination, either through seminar and conference presentations or through publications
• Present research findings, either at conferences or through publications in reputable outlets appropriate to the discipline
• Contribute to grant applications submitted by others and develop own research objectives and proposals for funding

The Person

Knowledge, Skills and Experience

• Experience in designing and constructing semantic data models, ontologies, and knowledge graphs to represent complex information, with focus on modelling urban domains and understanding complex socio-technical relationships
• A 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 in 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. Crucially, this includes experiences in analysing temporal data derived from complex urban environments and an understanding of methods for integrating dynamic, real-world urban observational data with structural knowledge bases
• Ability to communicate complex information with clarity to diverse stakeholders across academia, industry, and public governance
• Experience of presentations at conferences and/or in high quality publications

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 fields (Research Associate)
• Near completition of PhD in Computer Science, Data Science, Urban Analytics, Environmental Engineering, or a closely related fields (Research Assistant)

[Employer hidden — sign up to reveal] 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 as well as celebrate, support and thrive on the contributions of all of our employees and the communities they represent. We are proud to be an equal opportunities employer and encourage applications from individuals who can complement our existing teams, we believe that success is built on having teams whose backgrounds and experiences reflect the diversity of our university and student population.

At [Employer hidden — sign up to reveal] we hold a Gold Athena Swan award in recognition of our good employment practices for the advancement of gender equality. We also hold a Race Equality Charter Bronze award in recognition of our work towards tackling race inequality in higher education REC. We are a Disability Confident employer and will offer an interview to disabled applicants who meet the essential criteria for the role as part of the offer and interview scheme.

In addition, we are a member of the Euraxess initiative supporting researchers in Europe.