Research Fellow, Quantitative Modelling, Optimization & Data Analytics / ML

NATIONAL UNIVERSITY OF SINGAPORE; Singapore; On-site; SGD 60,000 - 90,000; Full time

Job summary

A leading academic institution in Singapore seeks a Research Fellow to support quantitative research in systems analysis and optimization. The ideal candidate will build models and analyze large datasets for operational planning across maritime contexts. Essential qualifications include a PhD in relevant fields, strong skills in optimization and machine learning, and proficiency in programming tools like Python and MATLAB. This role offers an engaging opportunity for research collaboration and contribution to publications.

Qualifications

• PhD in Industrial Engineering, Operations Research, Maritime Studies, Energy Systems, or related quantitative discipline.
• Strong background in optimization modelling.
• Experience with data analytics and machine learning methods.

Responsibilities

• Build optimization and analytical models for planning and operational decisions.
• Conduct scenario-based analysis under uncertainty.
• Analyze large operational datasets using statistical methods.

Skills

Optimization modelling

Data analytics

Machine learning

Analytical skills

Python

MATLAB

Education

PhD in Industrial Engineering or related

Tools

Python

MATLAB

R

Excel

Job description

Interested applicants are invited to apply directly at the NUSCareer Portal

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

We regret that only shortlisted candidates will be notified.

Job Description

The Centre for Maritime Studies at the National University of Singapore (NUS) invites applications for a Research Fellow to support quantitative research in systems analysis, optimization, and data-driven decision support. The role involves model development and empirical analysis for operational planning and technology-transition questions across applied domains (e.g., maritime).

Key Responsibilities

• Build optimization and analytical models for planning and operational decisions (e.g., asset renewal, technology adoption, resource allocation).
• Conduct scenario-based analysis under uncertainty (stochastic/robust/sensitivity approaches).
• Analyze large operational datasets using statistical and machine learning methods.
• Assess economic, environmental, and operational impacts of alternative strategies and regulatory/market constraints.

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., time-series forecasting, natural language processing, causal inference, generative AI).
• Uncertainty modelling (e.g., stochastic programming, robust optimization, scenario modelling).
• Proficiency in Python/MATLAB/R/Excel.
• Familiarity with maritime domain, operational datasets (e.g., AIS, IHS, SeaWeb) and energy-transition or policy modelling related to technology adoption and decarbonization measures is a strong plus.
• Strong publication record, analytical skills, and ability to communicate with both academic and policy audiences.