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

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

National University of SingaporeWorkFromHome, Otago, New Zealand
9 days ago
Job description

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

National University of Singapore – Queenstown

The National University of Singapore is the national research university of Singapore. Founded in 1905 as the Straits Settlements and the Federated Malay States Government Medical School, NUS is the oldest higher education institution in Singapore.

Model Development

  • Develop and calibrate urban building energy models using physics‑based simulation tools.
  • Integrate AI and machine learning methods (e.g., surrogate modelling, generative design) to enhance simulation efficiency and scalability.

Carbon Emissions Analysis

  • Estimate both operational and embodied carbon emissions at building and district scales.

    • Data Management & Integration

  • Compile, clean, and manage multi‑source datasets (geospatial, climate, building archetypes, construction materials, energy use).
  • Develop reproducible workflows for integrating physics‑based and AI‑driven approaches.

    • Research & Dissemination

  • Publish findings in high‑impact peer‑reviewed journals and present at international conferences.
  • 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, including organising and participating in meetings, workshops, and knowledge‑sharing sessions.

    • Job Requirements

  • 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 ID : 30802

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    Research • WorkFromHome, Otago, New Zealand