Research Fellow (Supply Chain Management)

National University of Singapore; Singapore; Sur place; SGD 78 000 - 112 000; Plein temps

Résumé du poste

The National University of Singapore invites applications for a Research Fellow in Supply Chain Management at The Logistics Institute - Asia Pacific, Kent Ridge Campus. You will design optimisation models, develop ML classifiers, and implement data-driven solutions for real-world transportation contexts.

We seek a PhD holder with 1–2 years of relevant experience in logistics research, and strong skills in MILP, simulation, Python, and data visualization.

Qualifications

• PhD in industrial engineering, OR equivalent field.
• 1–2 years in logistics/supply chain or related research.
• End-to-end model delivery: data pipeline, algorithm design, validation.

Responsabilités

• Design and implement optimisation models for transport contexts.
• Develop heuristics and solvers for large-scale problems.
• Create MILP formulations for vehicle-task assignment.
• Develop simulation data science solutions for complex processes.
• Analyse historical and real-time data for insights.

Connaissances

Logistics & SCM experience

Optimization model design

Heuristics & solvers

MILP modelling

Discrete-event simulation

Data analysis

Machine learning

Explainability tools

Data visualization

Formation

PhD in Industrial Engineering

Outils

Gurobi

CPLEX

OR-Tools

AnyLogic

AnyLogistix

FlexSim

Arena

AIMMS

Demo 3D

Python PuLP

Power BI

Tableau

SHAP/LIME

Description du poste

Research Fellow (Supply Chain Management)

University-Level Unit: The Logistics Institute - Asia Pacific

Faculty/Department-Level Unit: Research

Employee Category: Research Staff

Location: Kent Ridge Campus

Posted On: 16/04/2026

Job Role/ Core Capabilities

• Experience with logistics and supply chain processes, including transport operations, fleet management, and operational readiness frameworks
• Model & Algorithm Development: Design, build and implement optimisation models for various transportation contexts, and machine learning classifiers
• Heuristics & Solvers: Develop and refine custom heuristics and metaheuristics (e.g., Tabu Search, Genetic Algorithms) to find high-quality solutions for large-scale, real-world problems. Utilise commercial and open-source solvers (e.g., Gurobi, CPLEX, Google OR-Tools).
• Formulate and solve Mixed-Integer Linear Programmes (MILP) for real-time vehicle-task assignment including objective function design and constraint modelling
• Design, implementation, and delivery of simulation data science solutions to perform system-of-systems discrete event simulations for significantly complex operational processes (e.g., AnyLogic, AnyLogistix, FlexSim, Arena, Demo 3D, etc.)
• Data Analysis & Feature Engineering: Analyse historical and real-time data from multi-source inputs
• Experience analysing large datasets and applying data-cleaning techniques along with performing statistical analyses leading to the understanding of the structure of datasets
• Machine Learning & AI: Train, evaluate, and deploy supervised classification models (e.g., XGBoost, LightGBM), design ensemble pipelines combining rule-based, probabilistic, and ML components; apply explainability tools (e.g., SHAP, LIME) to produce interpretable, auditable decision outputs
• Experience with data visualisation and visualisation tools (e.g., MS Power BI, Tableau), including design and delivery of operational KPI dashboards covering driver-task mismatch rates, fleet health distribution, fatigue heatmaps, and dispatch allocation efficiency

Requirements

Education

• PhD in industrial engineering, Operations Research, Computer Science, Operations Research or an equivalent field

Experience

• Min 1-2 years of experience in logistics and supply chain management
• Experience in research or applied engineering environment delivering optimisation, simulation, or decision-support systems for operational contexts
• Track record of end-to-end model delivery: problem formulation: data pipeline, algorithm design, implementation, validation, stakeholder communication

Technical Skills

• Experience or familiarity with optimisation packages such as Gurobi, CPLEX, OR-Tools, and Python PuLP for MILP formulation and solver configuration
• Experience or familiarity with simulation tools such as AnyLogic, AnyLogistix, FlexSim, Arena, AIMMS, Demo 3D or similar tools
• Proficiency in Python including pandas, numpy, scipy, scikit-learn, XGBoost, PuLP/OR-Tools, and pytest; proficiency in SQL for data queries and operational log extraction
• Experience with MS Power BI, Tableau
• Familiarity with MLflow or equivalent experiment tracking tools for ML retraining pipelines
• Proficiency in MS Office

Req ID: 32507