Job Description

We are looking for a full-time Research Associate to work in the Urban Climate Resilience module of the Future Resilient Systems programme, with a focus on Digital Twin-Enabled District Energy System Resilience. The appointment is for at most 3 months. The appointment is expected to start July 2025.

This collaborative research concerns the development of a digital twin for urban climate resilience, especially with respect to building lifecycle analysis emphasizing operational GHG emissions and embodied carbon, and interactive decision support. As a first pilot study, a digital twin of district energy systems was created for the NUS campus to investigate how past events and possible future scenarios can affect the resilience of these systems. This digital twin comprises a data visualization platform and building energy demand models for scenario assessment. Electricity and cooling meter data were collected for several buildings on campus, as well as WiFi connection logs providing an estimate for the number of occupants in each building. Analyzing the data showed that buildings’ cooling systems are operated in a relatively centralized way, meaning that buildings consume energy for cooling whether they are occupied or not. To explore future scenarios for the case study area, these meter data were used to calibrate building energy demand models for a baseline year. Standards were used for other buildings to make appropriate assumptions for the needs for different building typologies. The platform allows to display these simulation results in a variety of ways, such as by typology, by energy use intensity, or by energy use per capita. Different forecast scenarios have been considered to investigate how different building system operation modes can support the transition to flexible work arrangements post-COVID. The scenarios show there is a significant potential for energy savings by operating buildings in a more occupant-driven way, where only occupied workspaces are actively conditioned.
To expand the platform, additional use cases have been considered an the study area has been expanded to city-scale. While the actual digital twin platform has so far emphasized operational GHG emissions, you would contribute to extending the analysis and visualization to life-cycle analysis, including embodies carbons, and flows of energy and materials.
 

Qualifications

• Candidate must have a Master’s degree with at least 2 years of relevant work experience or at least 2 years’ experience as a fulltime PhD student and be expected to complete a PhD degree within six months of starting the appointment 
• Good time management and planning;
• Excellent command of both spoken and written English;
• Highly motivated individual with capacity to work independently;
• Scripting/computer programming knowledge is required;
• Knowledge of data analytics and/or data visualization is desired;
• Knowledge of life-cycle analysis or material stock modelling is desired;
• Expertise in building simulation and/or the application of deep learning is a plus.

More Information

Location: Kent Ridge Campus

Organization: College of Design and Engineering

Department : Architecture

Employee Referral Eligible: No

Job requisition ID : 28454