Organized by the Administration of Commerce, Ministry of Economic Affairs, and implemented by the AI Talent Office for the Commercial Service Industry in collaboration with the National Yunlin University of Science and Technology (YunTech), the AI-Powered Smart Logistics Training Program for Young Professionals aims to cultivate interdisciplinary talent with expertise in artificial intelligence, data analytics, and smart logistics applications.Applications are now open to recent university graduates (bachelor's degree or above) and job seekers aged 18 to 35.
The program adopts a two-stage training model:
Stage 1: AI Smart Logistics Applications (120 Hours)
Participants will receive comprehensive training covering:
- Fundamentals of artificial intelligence
- Python programming
- Machine learning
- Data analytics
- Google Colab applications
- Hands-on smart logistics case studies
Stage 2: Industry Project Practicum (120 Hours)
Participants will work on real-world industry projects based on practical logistics scenarios, including:
- Improving warehouse order-picking efficiency
- Intelligent scheduling
- Product demand hotspot prediction
- Delivery route optimization
Participants who complete Stage 1, achieve at least 90% attendance, and pass the post-training assessment may apply for a training incentive of NT$20,000.
Those who successfully complete Stage 2 and receive approval from an evaluation panel of academic and industry experts may apply for an additional NT$30,000 incentive, for a maximum total training incentive of NT$50,000.
In addition to technical training, the program provides industry-based projects, mentorship from experienced professionals, résumé reviews, interview coaching, and career matching services to help participants pursue careers in smart logistics, supply chain management, data analytics, and AI applications.
Application Website:
https://clasai202607.github.io/classai202607.github.io/
As places are limited, applicants will be selected based on eligibility requirements and the results of the application review process.

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