Opening Positions

✅  We are always searching for talented Ph.D. Students in the area of “AI+OR.” Please send me your CV, transcripts, and a brief explanation of research interests (shaoclin@hku.hk). Outstanding RPg candidates can apply Hong Kong PhD Fellowship Scheme (HKPF) and HKU Presidential PhD Scholar Programme (HKU-PS).  

✅ The information for Research Assistant positions:

1️⃣ The positions are under the research area of "Data-driven Decision Making: Leveraging Machine Learning for Operational Excellence." Related research projects include:

• "Data-Driven Inventory Management with Financial Hedging," GRF, 2025.1.1- 2026.12.31

• "Data-Driven Inventory, Pricing, and Promotion Optimization with Proxy Covariates," NSFC, 2025.1.1-2027.12.31 

Sample papers can refer to:

• “Data-driven newsvendor problems regularized by a profit risk constraint,” Link to SSRN.

•  "A Semi-parametric framework for data-driven inventory management with financial hedging," Link to SSRN. 

• "Contextual preference learning for dynamic assortment and inventory planning," Link to SSRN. 

• "Contextual stochastic optimization under confounding effects," Link to PDF. 

If interested, please send your CV to Dr. Yulong Huang (yulhuang@hku.hk)  and cc to Prof. Lin (shaoclin@hku.hk).

2️⃣ 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:

a) Semiconductor Equipment Scheduling: In semiconductor fabs, a vast number of wafers are processed across hundreds or even thousands of manufacturing tools following highly complex workflows. We aim to develop optimization models and algorithms to improve wafer processing sequences across semiconductor manufacturing tools, with the objectives of reducing cycle times and lowering work-in-progress (WIP) inventory, thereby increasing throughput and overall manufacturing efficiency.

b) Automated Material Handling System (AMHS) Scheduling: In modern semiconductor fabs, AMHS, typically realized through Overhead Hoist Transport (OHT), is responsible for transferring wafers between manufacturing tools. Given the massive production scale, the AMHS must handle tens of thousands of transport requests every day, ensuring timely and reliable delivery of wafers to the correct tools. Our research seeks to develop efficient and adaptive scheduling algorithms for AMHS systems to significantly reduce transport delays, improve material flow, and enhance coordination between logistics and manufacturing processes.

c) Digital Twin System: We aim to construct a digital twin framework that integrates real-time data and simulation models to mirror the physical manufacturing and logistics systems. This enables performance monitoring, predictive analysis, and the evaluation of scheduling strategies in a virtual environment, thereby supporting more robust and adaptive decision-making in semiconductor manufacturing.

Responsibilities:

a) Closely collaborate with researchers in the group to develop efficient algorithms for the above problems.

b) Deliver research outcomes to our industry partners, to support practical applications in semiconductor manufacturing.

c) Write and submit high-quality academic papers.

Qualifications：

a) Applicants must have obtained a bachelor's or master's degree in Automation, Computer Science, Management Science, Industrial Engineering, or other related fields, or currently be a PhD student.

b) Applicants must be proficient in at least one programming language (e.g., C++, Python) and meet at least one of the following criteria:

• Familiarity with modeling and common solution methods for scheduling and path planning problems, and experience with commercial solvers such as CPLEX or GUROBI. Prior experience in publishing academic papers is preferred.

• Strong programming skills, with preference given to those with software development experience or industry work experience.

If interested, please send your CV to Dr. Anbang Liu (anbang@hku.hk)  and cc to Prof. Lin (shaoclin@hku.hk).

3️⃣ The positions are under the project of "AI-Driven Supply Chain Decision Platform." The positions are dedicated to exploring and pushing the boundaries of intelligent transformation within the supply chain domain, focusing on the theoretical research and innovative application of Large Language Models (LLMs) in complex supply chain environments. You will be responsible for designing and exploring cutting-edge architectures for supply chain LLMs, deeply engaging in in-depth performance optimization and iterative enhancement of models, and conducting innovative research and theoretical integration of supply chain-related algorithms. The core objective of this role is to produce forward-looking research outcomes, including theoretical models, experimental validation prototypes, academic publications (in top-tier conferences or journals), and potentially, technology transfer solutions that can drive industrial upgrading.

We are seeking outstanding researchers with strong research experience in Automation, Industrial Engineering, Systems Engineering, Computer Science, or related fields. Applicants must possess a profound theoretical and practical understanding of deep learning, particularly in the architecture, training, and optimization of LLMs. Proficiency in programming (e.g., Python) and hands-on experience with mainstream deep learning frameworks (e.g., TensorFlow, PyTorch) or practical experience with open-source LLMs are essential. Candidates with demonstrated experience in cutting-edge research on LLMs and a strong record of high-impact academic publications in relevant fields will be given preferential consideration.

If interested, please send your CV to Dr. Pujun Zhang (pjzhang@hku.hk)  and cc to Prof. Lin (shaoclin@hku.hk).

4️⃣ The positions are under the project "New-Generation Smart Manufacturing ERP." The research focuses on smart decision-making driven by dynamic domain knowledge and chain business scenarios, integrating real-time sensing, knowledge automation, and predictive modeling. It includes four key tasks: real-time sensing and aggregation of multi-source dynamic demands; automated domain knowledge extraction, representation, and updates using LLMs; business rule generation and dynamic updates through associative knowledge mining; and multi-scenario decision analysis and demand matching using predictive models. The project leverages advanced AI techniques such as deep learning, knowledge graphs, and federated learning to create an adaptive and robust framework for complex domains. Importantly, the project aims to produce high-quality papers (patents/software development)!

If interested, please send your CV to Dr. Yuan Qu (yuanqu@hku.hk)  and cc to Prof. Lin (shaoclin@hku.hk).

✅ The information for Postdoc positions: 

5️⃣  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:

a) Semiconductor Equipment Scheduling: In semiconductor fabs, a vast number of wafers are processed across hundreds or even thousands of manufacturing tools following highly complex workflows. We aim to develop optimization models and algorithms to improve wafer processing sequences across semiconductor manufacturing tools, with the objectives of reducing cycle times and lowering work-in-progress (WIP) inventory, thereby increasing throughput and overall manufacturing efficiency.

b) Automated Material Handling System (AMHS) Scheduling: In modern semiconductor fabs, AMHS, typically realized through Overhead Hoist Transport (OHT), is responsible for transferring wafers between manufacturing tools. Given the massive production scale, the AMHS must handle tens of thousands of transport requests every day, ensuring timely and reliable delivery of wafers to the correct tools. Our research seeks to develop efficient and adaptive scheduling algorithms for AMHS systems to significantly reduce transport delays, improve material flow, and enhance coordination between logistics and manufacturing processes.

c) Digital Twin System: We aim to construct a digital twin framework that integrates real-time data and simulation models to mirror the physical manufacturing and logistics systems. This enables performance monitoring, predictive analysis, and the evaluation of scheduling strategies in a virtual environment, thereby supporting more robust and adaptive decision-making in semiconductor manufacturing.

Responsibilities:

a) To address the above challenges, applicants are expected to formulate mathematical models of the problems and develop efficient solution methods, particularly by leveraging techniques from machine learning and operations research.

b) The applicants are expected to deliver research outcomes to our industry partners, to support practical applications in semiconductor manufacturing.

c) The applicants are expected to write and submit high-quality academic papers.

Qualifications：

a) Education: Ph.D. in Automation, Industrial Engineering, Systems Engineering, Computer Science, or a closely related field.

b) Technical Skills: Strong programming proficiency is required.

c) Research Experience: Relevant research experience in related problem domains, or research experience in scheduling, routing, and multi-agent pathfinding (MAPF) algorithms.

    - Experience across multiple areas is a strong plus.

    - Experience developing ML-based optimization approaches is a strong plus. 

    - A strong publication track record is highly desirable.

d) Industry collaboration experience: Prior experience in collaborative R&D with industry partners is a strong plus.

If interested, please send your CV along with 3 reference letters to Dr. Anbang Liu (anbang@hku.hk)  and cc to Prof. Lin (shaoclin@hku.hk).

6️⃣  The position is under the project of "AI-Driven Supply Chain Decision Platform." The position is dedicated to exploring and pushing the boundaries of intelligent transformation within the supply chain domain, focusing on the theoretical research and innovative application of Large Language Models (LLMs) in complex supply chain environments. You will be responsible for designing and exploring cutting-edge architectures for supply chain LLMs, deeply engaging in in-depth performance optimization and iterative enhancement of models, and conducting innovative research and theoretical integration of supply chain-related algorithms. The core objective of this role is to produce forward-looking research outcomes, including theoretical models, experimental validation prototypes, academic publications (in top-tier conferences or journals), and potentially, technology transfer solutions that can drive industrial upgrading.

We are seeking outstanding researchers holding a PhD (or equivalent research experience) in Automation, Industrial Engineering, Systems Engineering, Computer Science, or related fields. Applicants must possess a profound theoretical and practical understanding of deep learning, particularly in the architecture, training, and optimization of LLMs. Proficiency in programming (e.g., Python) and hands-on experience with mainstream deep learning frameworks (e.g., TensorFlow, PyTorch) or practical experience with open-source LLMs are essential. Candidates with demonstrated experience in cutting-edge research on LLMs and a strong record of high-impact academic publications in relevant fields will be given preferential consideration.

If interested, please send your CV along with 3 reference letters to Dr. Pujun Zhang (pjzhang@hku.hk)  and cc to Prof. Lin (shaoclin@hku.hk).

7️⃣ The position is under the HKU DASE Postdoctoral Academic Development (D-PAD) scheme.

The eligibility criteria for D-PAD applicants are as follows:

1. The applicant must have a PhD degree in a relevant field from a reputable university;

2. The applicant should have a strong publication record, including at least one SCI/SSCI journal paper published or accepted;

3. The applicant must demonstrate excellent English proficiency, as evidenced by either of the following:

   - A PhD degree from an English-teaching university (overseas or in Hong Kong),

   Or

   - At least 2 years of academic experience at an English-teaching university (overseas or in Hong Kong),

   Or

   - An IELTS score of 7.0 or higher (with subtest scores above 6.5, taken within the past 2 years);

The D-PAD position appointment will be full-time for 12 months, with the possibility of renewal based on satisfactory performance. The D-PAD's responsibilities will include assisting with teaching, learning, and research activities within our group's research team. In particular, the D-PAD position is responsible for helping advise 10 MSc dissertation students.

If interested, please send your CV along with 3 reference letters to Dr. Yuan Qu (yuanqu@hku.hk)  and cc to Prof. Lin (shaoclin@hku.hk).