Research Engineer / Research Assistant (HVAC AI-Driven Energy Optimization)

National University of Singapore; Singapore; On-site; SGD 60,000 - 90,000; Full time

Job summary

National University of Singapore (NUS) at Kent Ridge Campus seeks a Research Engineer (RE) or Research Assistant (RA) to develop AI-driven energy optimization for industrial HVAC systems. The role emphasizes deployment on industrial PCs, data integration from multiple HVAC components, and validation against baseline energy profiles.

The candidate should have a strong HVAC background, familiarity with BMS architectures and protocols, and experience in Python/C++/MATLAB with embedded systems.

Qualifications

• Bachelor’s degree with strong academic record and technical HVAC background.
• Solid working knowledge of chiller plants, AHUs, CTs, pumps and control logics.
• Familiarity with BMS architectures and industrial protocols (BACnet, Modbus, OPC-UA).
• Proficiency in Python, C++, or MATLAB; experience with IPCs/edge devices is advantageous.

Responsibilities

• Deploy AI-driven HVAC optimization algorithms on IPCs for real-time edge execution.
• Build data pipelines from chillers, AHUs, CTs, and pumps using BMS/Modbus/BACnet/OPC-UA.
• Integrate and validate control parameters in the BMS to reduce energy use.
• Conduct field testing, calibration, and performance validation against baselines.
• Assist in documentation, reports, and academic publications.

Skills

Python

C++

MATLAB

Embedded systems

Education

Bachelor’s degree in Electrical Engineering or related

Tools

BACnet

Modbus

OPC-UA

PLC/SCADA

Job description

Research Engineer / Research Assistant (HVAC AI-Driven Energy Optimization)

Location: Kent Ridge Campus

This is a full-time position with a contract duration of one (1) year, renewable subject to performance and funding availability.

Job Description

The Department of Electrical and Computer Engineering (ECE) at the National University of Singapore (NUS) is seeking a highly motivated Research Engineer (RE) or Research Assistant (RA) to join our team. This role focuses on the development, deployment, and validation of next-generation, AI-driven energy optimization solutions for industrial HVAC systems.

Core Responsibilities

• Algorithm Deployment: Implement, test, and validate AI-driven HVAC optimization algorithms on industrial PC (IPC) platforms for real-time edge execution.
• Data Integration: Establish data acquisition pipelines from chillers, Air Handling Units (AHUs), Cooling Towers (CTs), and pumps using BMS/BACnet/Modbus protocols.
• Closed-Loop Control: Inject computed optimal parameters back into the active Building Management System (BMS) to achieve measurable energy reductions.
• Field Testing & Auditing: Conduct on-site commissioning, calibration, and rigorous performance validation against established baseline energy profiles.
• Hardware Evolution: Progressively design and integrate embedded hardware systems aimed at eventual full-scale replacement of legacy BMS units.
• Documentation & Support: Draft technical documentation, project reports, and high-impact academic publications while supporting peer researchers on field activities.

Qualifications

• Min a Bachelor’s degree (BEng/BSc) with a strong academic record and a proven technical background in HVAC systems.
• HVAC Systems: Solid working knowledge of chiller plants, AHUs, cooling towers, hydraulic pumps, and their sequential control logics.
• Industrial Communication: Familiarity with BMS architectures and industrial protocols (BACnet, Modbus, OPC-UA) or PLC/SCADA integration.
• Software & Hardware: Intermediate proficiency in Python, C++, or MATLAB. Hands-on experience with industrial PCs, edge gateways, or embedded controllers is highly advantageous.
• Industry Exposure (Desirable): Prior experience in building energy audits, retro-commissioning, or smart building management systems.
• Soft Skills: Professional proficiency in English with strong technical writing and verbal presentation capabilities.

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