Job Description

Model Development:
•    Develop and train surrogate ML models (e.g., neural networks, Gaussian processes, gradient boosting) to emulate computationally intensive building and urban performance simulations — including EnergyPlus (thermal/energy), Radiance/Daysim (daylighting), and CFD tools (airflow/microclimate) — enabling real-time performance feedback within agentic design workflows.
•    Design and implement agentic AI workflows for building and urban design tasks, including multi-step reasoning pipelines, tool-calling agents
•    Integrate large language models (LLMs) with physics-based simulation tools and parametric design environments (e.g., Rhino/Grasshopper, EnergyPlus, QGIS) to automate and accelerate performance-driven design workflows.

Platform and API Development:
•    Build and maintain backend services, APIs, and user-facing interfaces that expose AI research outputs to end users in the architecture, engineering, and construction (AEC) sector; contribute to open-source tools and research prototypes developed within the City Syntax Lab.
•    Develop and evaluate benchmarks for LLM spatial reasoning in building and urban contexts, contributing to academic research on AI capabilities and limitations in domain-specific design and simulation tasks.

Research & Dissemination:
•    Publish research findings and present at academic conferences; document tools, experiments, and evaluation results to ensure reproducibility and support knowledge transfer within the lab and to external collaborators.

Job Requirementss

Essential:
•    Bachelor’s or Master’s degree in Computer Science, Software Engineering, Building/Urban Science, Electrical Engineering, Data Science, or a related field.
•    Proficiency in software engineering best practices including version control (Git), containerisation (Docker), CI/CD pipelines, and API design; experience building and deploying production-ready or near-production applications is strongly preferred.
•    Hands-on experience building LLM-powered applications, including prompt engineering, function/tool calling, agentic pipelines
•    Strong Python programming skills; experience with web frameworks and API development (e.g., FastAPI, Flask) and/or frontend development (React, TypeScript) is advantageous.
•    Experience with life-cycle carbon assessment methods and tools.
•    Genuine interest in sustainable built environments, climate technology, or smart cities; prior exposure to AEC software (e.g., Rhino, Grasshopper, Revit, EnergyPlus) or geospatial tools (QGIS, GIS APIs) is a plus but not required.
•    Ability to work in a fast-paced, applied research environment, take ownership of projects, and translate academic research prototypes into functional, deployable tools.
•    Strong communication skills and ability to work collaboratively across a multi-disciplinary team spanning AI research, building science, and urban planning.