Research Fellow (Hybrid AI + Physics-Based Urban Modeling)
Location: Kent Ridge Campus
Organization: College of Design and Engineering
Department: The Built Environment
Employment type: Full-time
Seniority level: Not Applicable
Job requisition ID: 30051
Responsibilities
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.
Estimate both operational and embodied carbon emissions at building and district scales.
Incorporate various dataset
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.
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.
Coordinate with project partners, government agencies, and industry stakeholders, including organising and participating in meetings, workshops, and knowledge-sharing sessions.
Qualifications
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.
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