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

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.

More Information

Location: Kent Ridge Campus

Organization: Centre for Maritime Studies

Department: Centre for Maritime Studies

Employee Referral Eligible: No

Job requisition ID: 31597

Apply now