Total 2 supply chain operator job vacancies in Education / Training category

Interested applicants are invited to apply directly at the NUS Career Portal. Please note your application will only be processed if you apply via NUS Career Portal.

NUS Career Portal link: https://careers.nus.edu.sg/job/Research-Fellow-%28Supply-Chain-Management%29/32507-en_GB/

We regret that only shortlisted candidates will be notified.

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

Job Summary

We are seeking a highly organized and technology-driven professional to oversee inventory management within a semiconductor cleanroom environment. This role ensures accurate tracking and control of cleanroom materials such as wafers, FOUPs/FOSBs, chemicals, and consumables, while leveraging AI and digital platforms to improve operational efficiency, traceability, and inventory optimization.

--------------------------------------------------------------------------------

Key Responsibilities

FAB cleanroom Inventory and Materials Management

• Oversee and maintain accurate inventory records for cleanroom materials including wafers, FOUPs/FOSBs, chemicals, spare parts and consumables.
• Track material movements across facilities, labs, or cleanroom areas and ensure proper documentation.
• Conduct routine inventory checks, cycle counts, and reconciliation to maintain data accuracy.
• Monitor stock levels and coordinate with planning, procurement and operations teams to prevent shortages or excess inventory.
• Ensure proper storage, labelling, and handling of cleanroom materials in compliance with contamination control standards.

Digitalization & AI Integration

• Implement and utilize digital platforms or AI tools to enhance inventory tracking and traceability.
• Develop digital dashboards and automated reports for real-time visibility of inventory status.
• Apply data analytics or AI-assisted forecasting to optimize inventory planning and usage.
• Support the integration of inventory data with ERP, MES, or digital supply chain systems.

Process Optimization

• Identify opportunities and initiate, to automate or digitalize manual inventory processes within the cleanroom environment.
• Improve material flow efficiency between cleanrooms, labs, and storage locations.
• Support initiatives related to smart manufacturing, Industry 4.0, or digital transformation.

Compliance & Documentation

• Ensure inventory practices comply with cleanroom protocols, safety standards, and internal audit requirements.
• Maintain proper documentation for material movement, storage, and disposal.
• Support SOP development and process documentation for inventory management systems.

--------------------------------------------------------------------------------

Preferred Skills

• Experience managing FOUPs, FOSBs, wafer logistics, spart parts or chemical inventories.
• Familiarity with cleanroom contamination control practices.
• Knowledge of digital supply chain, smart manufacturing, or Industry 4.0 technologies.
• Ability to develop data-driven insights for operational improvements.

--------------------------------------------------------------------------------

Requirements

• Diploma or Degree in Supply Chain Management, Engineering, Semiconductor Technology, Data Analytics, or related field.
• 2–5 years of experience in inventory control, logistics, or operations within a cleanroom or semiconductor environment.
• Familiarity with cleanroom protocols and handling of semiconductor materials.
• Experience with ERP, MES, or inventory management systems.
• Knowledge of AI tools, digital dashboards (e.g., Power BI), automation platforms, or data analytics tools is advantageous.
• Strong analytical skills with attention to detail and process improvement positive mindset.