Research Fellow, Built Environment

Job Description

The focus of this position is the development of a learning-based framework to optimize the data acquisition for building energy model calibration. Successful digital twin application relies on a properly calibrated model, where measured data from actual buildings is indispensable. However, building operational data is typically sparse and restricted within narrow operation conditions, containing limited information about the complex building dynamics. Current research in the discipline of model calibration often focuses on the calibration algorithm and model structure, while data acquisition remains ad-hoc and highly expert-driven. Data acquisition is usually a laborious and expensive process when calibrating building energy models. Additionally, processing large amounts of data is often time-consuming and may distract the energy modeler from key information regarding the parameters of interest. In the proposed framework, a data-centric paradigm of digital twin calibration is adopted. The algorithm aims to inform an optimal experimental design that would minimize the reducible or epistemic uncertainties in an energy model.

Job Requirements

Desired candidates should be familiar with the thermodynamics in buildings and the modelling tools such as Modelica and EnergyPlus. The position will require experience in calibrating building energy models with actual operational data from building management systems (BMS). The candidates should have previous experience of applying and implementing machine learning models. The candidates should have experience of using a real-time database for building and ACMV systems, preferably through an application programming interface (API).

The candidate should have a Bachelor’s and Master’s degree in Engineering, Architecture, or Computer Science and should have a PhD in similar fields, or be nearing completion of a PhD degree. The candidate should be an expert in programming languages such as Python and R, advanced machine learning method, and building simulation techniques.

Covid-19 Message

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Taking into consideration the health and well-being of our staff and students and to better protect everyone in the campus, applicants are strongly encouraged to have themselves fully COVID-19 vaccinated to secure successful employment with NUS.

More Information

Location: Kent Ridge Campus
Organization: College of Design and Engineering
Department: The Built Environment
Employee Referral Eligible: No
Job requisition ID: 16487