Research Assistant (Energy Management and Information System)

University-Level Unit:
College of Design and Engineering

Faculty/Department-Level Unit:
The Built Environment

Employee Category:
Research Staff

Location:
Kent Ridge Campus

Posting Start Date:
19/03/2026

Job Description

The Building Informatics and Operations Research (BIOR) Lab at the National University of Singapore (NUS) is looking for one outstanding and highly motivated Research Assistant to work on Digital Twin-enabled Energy Management and Information System (EMIS). The research assistant will be advised by Assistant Professor Maomao Hu from the Department of the Built Environment, College of Design and Engineering, NUS.

Responsibilities:
• With minimum supervision, perform research and analyses in the areas of digital twin, EMIS, and data analytics.
• Support the development and implementation of digital twin-enabled frameworks for building energy monitoring, diagnostics, and operational optimization.
• Develop, integrate, and maintain data pipelines for collecting, storing, and analyzing building operational data from sensors, meters, and building management systems.
• Support the design and deployment of visualization dashboards and decision-support tools for EMIS and digital twin applications.
• Assist in the development of data-driven and physics-informed models for building energy prediction, fault detection, and control applications.
• Work closely with Postdocs and PhD students within the research group and the Centre for Digital Building Technology.

Qualifications

Required qualifications and skills:
• Master’s degree in Civil, Mechanical, Electrical, Architectural, or related engineering disciplines.
• Proficiency in programming and data analysis tools, such as Python, MATLAB, or R.
• Demonstrated experience in digital twin, EMIS, and data analytics.
• Demonstrated experience in database management, dashboard development, and cloud platforms.
• Familiarity with sensors, building automation systems, building communication protocols (e.g., BACnet, Modbus, KNX), and IoT-based data acquisition. Basic knowledge of machine learning and building controls.
• Strong communication, teamwork, and organizational skills.

How to Apply:

For first consideration, please submit the following application materials online:
• Curriculum Vitae
• Cover Letter describing your research interests and the relevance of your background
Applications will receive full consideration until the positions are filled and only shortlisted applicants will be contacted. For enquiries, please contact Assistant Professor Maomao Hu at maomaohu@nus.edu.sg.

Apply now