Research Fellow (Maritime Decarbonization)

NATIONAL UNIVERSITY OF SINGAPORE; Singapore; On-site; SGD 50,000 - 70,000; Full time

Job summary

A leading research university in Singapore is seeking a Research Fellow to support analytical and modeling work in maritime decarbonization. The role focuses on quantitative analysis of fleet operations and energy transition strategies. The ideal candidate will hold a PhD in a relevant field and have a strong background in optimization modeling and data analytics. Responsibilities include developing models for fleet renewal and conducting scenario-based energy transition analyses.

Qualifications

• PhD in Industrial Engineering, Operations Research, Maritime Studies, or a related quantitative discipline.
• Strong background in optimization modelling, data analytics, and machine learning.
• Proficiency in programming for data analysis and modelling.

Responsibilities

• Develop analytical & optimization models for fleet renewal and fuel switching.
• Conduct scenario-based analysis of maritime energy transition pathways.
• Analyze vessel movement data using statistical and machine learning techniques.

Skills

Optimization modelling

Data analytics

Machine learning

Uncertainty modelling

Programming (Python/MATLAB/R/Excel)

Education

PhD in Industrial Engineering or related field

Tools

Python

MATLAB

R

Excel

Job description

Interested applicants are invited to apply directly at the NUS Career Portal

Your application will be processed only if you apply via the NUS Career Portal

We regret that only shortlisted candidates will be notified.

Job Description

We invite applications for the position of Research Fellow to support analytical and modelling work in maritime decarbonization. This position focuses on quantitative analysis of fleet operations, energy transition strategies, and cost-effective decarbonization planning for international and domestic shipping.

• Develop analytical & optimization models to support fleet renewal planning, fuel switching, and operational strategies under decarbonization scenarios.
• Conduct scenario-based analysis and modelling of maritime energy transition pathways, including uncertainty quantification using stochastic and robust methods.
• Analyze vessel movement data using statistical and machine learning techniques to characterize fleet operations, emission profiles, and port performance.
• Assess the economic, environmental, and operational impacts of maritime decarbonization pathways and regulatory measures, including frameworks such as the IMO Net-Zero Framework (NZF), carbon levies, and related compliance instruments.
• Contribute to publications, reports, and policy briefs, and collaborate with the research team in data collection and analysis.

Qualifications

• PhD in Industrial Engineering, Operations Research, Maritime Studies, Energy Systems, or a related quantitative discipline.
• Strong background in: Optimization modelling (e.g., mixed-integer programming, multi-stage planning). Data analytics and machine learning (e.g., clustering, regression, forecasting). Uncertainty modelling (e.g., stochastic programming, robust optimization, scenario modelling).
• Proficiency in programming (Python/MATLAB/R/Excel) for data analysis and modelling.
• Familiarity with maritime datasets (e.g., AIS, IHS, SeaWeb), vessel characteristics, or decarbonization technologies and measures is a strong plus.
• Strong publication record, analytical skills, and ability to communicate with both academic and policy audiences.
• Ability to work independently and collaboratively in a multi-disciplinary environment.
• (Preferred) Knowledge of Singapore/Asia maritime ecosystem and related industry datasets.