· Awards and Honors · 2 min read

Good News: Prof. Jianshe Feng's Course "Intelligent Decision-Making and System Reliability Engineering" Selected as a 2026 University-Enterprise Co-Built Core Course

The graduate course "Intelligent Decision-Making and System Reliability Engineering," led by AI Cube Lab head Prof. Jianshe Feng, has been selected as a 2026 University-Enterprise Co-Built Core Course, jointly developed with industry leaders including JLC Technology Group, Foxconn Industrial Internet, TDK SensEI, and Darry Electronics.

The graduate course "Intelligent Decision-Making and System Reliability Engineering," led by AI Cube Lab head Prof. Jianshe Feng, has been selected as a 2026 University-Enterprise Co-Built Core Course, jointly developed with industry leaders including JLC Technology Group, Foxconn Industrial Internet, TDK SensEI, and Darry Electronics.

The Graduate School recently announced the results of the 2026 University-Enterprise Co-Built Core Course selection, and the graduate course “Intelligent Decision-Making and System Reliability Engineering,” led by Prof. Jianshe Feng, head of the AI Cube Lab, has been successfully selected.

University-Enterprise Co-Built Core Courses are jointly developed by universities and enterprises, or independently developed by enterprises, with a focus on cultivating engineering practice ability and emphasizing industry relevance and technological cutting-edge. This is an important initiative of the School in advancing excellent engineer training and industry-education integration. The selected course, “Intelligent Decision-Making and System Reliability Engineering,” is jointly built with leading enterprises in electronics manufacturing and industrial internet — including JLC Technology Group, Foxconn Industrial Internet, TDK SensEI, and Darry Electronics — focusing on frontier technologies in intelligent decision-making and system reliability across the full industrial chain.

The course brings frontline enterprise engineering practice and the latest technology trends into the classroom, helping graduate students understand the core challenges of intelligent decision-making and reliability engineering in real industrial contexts, further deepening the integration of industry and education in the lab’s graduate training.

This selection marks another achievement in the lab’s continued commitment to industry-education integration and aligning course development with industry frontiers, providing graduate students with a learning platform even closer to industrial practice.

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