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

NATIONAL UNIVERSITY OF SINGAPORE; Singapore; On-site; SGD 60,000 - 80,000; Full time

Job summary

A leading academic institution in Singapore is seeking a Research Fellow to conduct quantitative research in systems analysis and optimization. The successful candidate will develop models and perform empirical analysis for operational planning in various applied domains. Applicants must have a PhD in a quantitative field, possess strong skills in optimization and data analytics, and be proficient in programming languages like Python or MATLAB. This role offers an exciting opportunity to contribute to innovative research and collaborations within the maritime industry.

Qualifications

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

Responsibilities

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

Skills

Optimization modelling

Data analytics

Machine learning

Uncertainty modelling

Python/MATLAB/R/Excel proficiency

Education

PhD in Industrial Engineering or related field

Tools

Statistical analysis software

Job description

Interested applicants are invited to apply directly at the NUSCareer Portal.

Your application will be processed only if you apply via 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.