Key Responsibilities:

1. Lead the design and optimization of algorithms for the company’s core AI-driven supply chain risk prediction and intelligent decision-making platform, building data-driven models for complex supply chain scenarios.

2. Develop and implement hybrid intelligent algorithms that integrate machine learning, operations research, and control theory to address key challenges in supply chain planning, scheduling, and real-time control.

3. Explore the application of cutting-edge technologies—including large language models (LLMs), graph neural networks (GNNs), and inverse reinforcement learning (IRL)—to enhance the intelligence of the system.

4. Design high-efficiency planning, scheduling, and real-time control systems for vertical industries such as semiconductor manufacturing, e-commerce logistics, and transportation networks, ensuring algorithms are applied in practical business operations.

5. Collaborate closely with the engineering team to translate research outcomes into scalable, reliable, industrial-grade solutions.

6. Monitor developments in both academia and industry, contribute to the company’s technology strategy, and drive intellectual property development and the commercialization of research results.

Minimum Entry Requirements:

1. Education and Research Background

1. PhD in Management Science and Engineering, Industrial Engineering, Operations Research/Optimization, Control Science and Engineering, Data Science, Computer Science, or a related field from a recognized university.

2. Postdoctoral research experience or equivalent research background, with a focus on supply chain management, logistics optimization, intelligent scheduling, transportation systems, smart manufacturing, or related domains.

3. Publication of representative research outcomes in high-level journals or international conferences in operations research, management science, supply chain, transportation, or intelligent decision-making.

2. Core Skills

1. Proficiency in operations research methods such as integer programming, dynamic programming, decomposition algorithms, and heuristic methods, with the ability to independently model and solve complex problems.

2. Solid understanding of reinforcement learning, inverse reinforcement learning, graph neural networks, and large language models, with practical application to planning, scheduling, or control problems.

3. Experience handling complex optimization challenges such as vehicle routing, production scheduling, resource allocation, and inventory control.

4. Ability to integrate optimization, learning, and control methodologies to solve practical intelligent decision-making problems.

3. Industry and Project Experience

1. At least 2 years of research or algorithm development experience; experience in internet, logistics, transportation, or manufacturing industries is preferred.

2. Participation in projects related to supply chain, logistics distribution, smart transportation, semiconductor manufacturing, or e-commerce operations, with experience translating models into operational systems.

3. Experience collaborating with industry partners or implementing research outcomes in practical applications.

4. Technical Tools and Soft Skills

1. Proficiency in Python, with strong algorithm implementation and engineering skills.

2. Familiarity with tools and frameworks such as Gurobi, CPLEX, PyTorch, and TensorFlow.

3. Strong English communication and technical writing skills.

4. Excellent teamwork and cross-department collaboration abilities, with a track record of driving projects to successful implementation.