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The University of Hong Kong
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Work type: Full-time
Department: Department of Data and Systems Engineering (14400)
Categories: Senior Research Staff & Post-doctoral Fellow, Research Staff
Location: Hong Kong
Job Description
We are seeking highly motivated researchers to join our project on “Planning and Scheduling of Automated Material Handling Systems in Semiconductor Manufacturing Environments.” This project is carried out in collaboration with leading semiconductor companies and targets real-world, industrially relevant challenges. Our goal is to advance cutting-edge research while ensuring strong practical applicability. The research will concentrate on three interrelated areas:
- Semiconductor Equipment Scheduling : Develop optimization models and algorithms to improve wafer processing sequences, reduce cycle times, and lower work-in-progress inventory to increase throughput and overall manufacturing efficiency.
- Automated Material Handling System (AMHS) Scheduling : Design efficient and adaptive scheduling algorithms for AMHS systems (typically realized through Overhead Hoist Transport) to reduce transport delays, improve material flow, and enhance coordination between logistics and manufacturing processes.
- Digital Twin System : Construct a digital twin framework that integrates real-time data and simulation models to mirror the physical manufacturing and logistics systems, enabling performance monitoring, predictive analysis, and evaluation of scheduling strategies in a virtual environment.
Responsibilities
- Formulate mathematical models of the problems and develop efficient solution methods, leveraging techniques from machine learning and operations research.
- Deliver research outcomes to industry partners to support practical applications in semiconductor manufacturing.
- Write and submit high-quality academic papers.
Qualifications
- Education : Ph.D. in Automation, Industrial Engineering, Systems Engineering, Computer Science, or a closely related field.
- Technical Skills : Strong programming proficiency.
- Research Experience : Relevant research experience in scheduling, routing, and multi-agent pathfinding (MAPF) algorithms; experience in ML-based optimization approaches; a strong publication track record is highly desirable.
- Industry Collaboration Experience : Prior experience in collaborative R&D; with industry partners is a strong plus.
Application
Send your CV, a cover letter, and contact information for three referees to Dr. Anbang Liu ( ) and cc to Prof. Lin ( ). Submit reference letters as soon as possible after applying.
Key Dates
Advertised: Nov 19, 2025 (HK Time)
Applications close: Mar 15, 2026 (HK Time)
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