Postdoctoral Research Fellow (Flexible and Resilient Energy Systems)

National University of Singapore; Singapore; Hybrid; SGD 60,000 - 85,000; Full time

Job summary

The National University of Singapore (NUS) invites two Postdoctoral Research Fellows to work on modeling, aggregation, and advanced control of distributed energy resources for energy flexibility and grid resilience. You will be advised by Assistant Professor Maomao Hu in the Built Environment department.

Responsibilities include developing scalable coordination algorithms, validating models via a digital twin, and publishing results in top journals.

Qualifications

• PhD in Engineering with strong training in optimization, control, and learning.
• Demonstrated modeling/analysis experience in power systems, building energy systems, or DERs (PV, EVs, batteries).
• Track record applying optimization, control, and learning to large-scale energy systems.
• Proficiency in Python, MATLAB, or R.
• Strong written and oral communication for publication and presentations.
• Experience collaborating in interdisciplinary research teams.
• Experience with distribution network simulation or digital twins is advantageous.

Responsibilities

• conduct independent research on DER flexibility characterization (PV, EVs, batteries, flexible loads).
• Develop scalable methods to aggregate DER flexibility into envelopes.
• Formulate optimization problems and scalable coordination algorithms for DER scheduling/dispatch.
• Integrate computational models into a high-fidelity digital twin platform and validate models with data and field results.
• Publish papers in high-impact journals and present findings at seminars/conferences.
• Collaborate with postdocs, PhD students, and researchers within the group and Centre for Digital Building Technology.

Skills

Optimization

Control theory

Learning methods

Python

MATLAB

Communication

Interdisciplinary teamwork

Education

PhD in Engineering

Optimization/Control focus

Tools

Python

MATLAB

R

Digital twins

Hybrid quantum-classical optimization

Job description

Company description

The National University of Singapore is the national research university of Singapore. Founded in 1905 as the Straits Settlements and the Federated Malay States Government Medical School, NUS is the oldest higher education institution in Singapore

Job description

The Building Informatics and Operations Research (BIOR) Lab at the National University of Singapore (NUS) is looking for two outstanding and highly motivated Postdoctoral Research Fellows to work on the modeling, aggregation, and advanced control of distributed energy resources (DERs) for energy flexibility and grid resilience. The postdoctoral research fellows 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, conduct independent research on flexibility characterization of DERs, including PV systems, EVs, battery energy storage systems, and flexible building loads.
• Develop scalable methods for aggregating heterogeneous DER flexibility into flexibility envelopes.
• Formulate optimization problems and develop scalable coordination algorithms for DER scheduling and dispatch.
• Develop and integrate computational models into a high-fidelity digital twin platform and validate flexibility models and optimization algorithms using operational data and field test results.
• Publish scientific papers in high-impact journals and present findings at seminars and conferences.
• Work closely with Postdocs, PhD students, and Research Assistants within the research group and the Centre for Digital Building Technology.

Job Requirements

Required Qualifications and Skills

• PhD in Electrical, Mechanical, Civil, Architectural Engineering, or other related fields with sufficient training in optimization, control, and learning.
• Demonstrated experience in modeling and analysis of power systems, building energy systems, or other DERs such as PV systems, EVs, and battery storage.
• A demonstrated track record in applying optimization, control, and learning to large-scale energy systems.
• Proficiency in statistical fundamentals and programming languages (e.g., Python, R, or MATLAB).
• Strong oral and written communication skills, including the ability to efficiently organize technical information for publication and presentation.
• Demonstrated interpersonal skills, including experience collaborating with a diverse interdisciplinary research team.
• Experience with distribution network simulation, large-scale mixed-integer optimization, and digital twins is advantageous. Familiarity with hybrid quantum-classical optimization is also desirable.