Research Fellow; Hybrid AI + Physics- Urban Modeling

Research Fellow (Hybrid AI + Physics-Based Urban Modeling)

Posting

Start Date:

18/08/2025

• Model Development:
  Develop and calibrate urban building energy models, integrate AI and machine learning methods (e.g., surrogate modelling, generative design) to enhance simulation efficiency and scalability.
• Carbon Emissions Analysis:
  Estimate operational and embodied carbon emissions at building and district scales, incorporating various datasets.
• Data Management & Integration:
  Compile, clean, and manage multi‑source datasets (geospatial, climate, building archetypes, construction materials, energy use) and develop reproducible workflows for integrating physics‑based and AI‑driven approaches.
• Research & Dissemination:
  Publish findings in high‑impact peer‑reviewed journals, present at international conferences, and contribute to policy briefs, technical reports, and outreach materials for industry and government stakeholders.
• Stakeholder Engagement & Coordination:
  Coordinate with project partners, government agencies, and industry stakeholders, organising and participating in meetings, workshops, and knowledge‑sharing sessions.

• PhD in Architecture, Building Science, Mechanical Engineering, Environmental, Urban Planning, Data Science, or related fields.
• Some experience in lifecycle analysis or carbon modelling, physics‑based building energy modelling and/or urban building energy modelling (UBEM).
• Strong programming skills (e.g., Python) for simulation automation, data analysis, and AI/ML workflows.
• Experience with life‑cycle carbon assessment methods and tools.
• Demonstrated track record of research excellence through publications and conference presentations.
• Familiarity with GIS tools and spatial data processing.
• Knowledge of urban sustainability, energy policy, and decarbonisation strategies.
• Ability to work in an interdisciplinary team and engage with external stakeholders.

• Location:
  
  Kent Ridge Campus
• Organization:
  College of Design and Engineering
• Department:
  The Built Environment
• Employee Referral Eligible:
  No
• Job requisition