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Frequently AskedQuestions

Find answers about joining the lab, undergraduate projects, graduate training, lab resources, and industry-academia collaboration.

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FAQ Category

About the Lab

5 frequently asked questions

iCPS Lab, the Intelligent Cyber-Physical Systems Laboratory, focuses on artificial intelligence, healthcare AI, and smart manufacturing. We integrate IoT, edge computing, and deep learning technologies, and apply research outcomes in real clinical and industrial settings to solve real-world problems.
The lab is led by Professor Wan-Jung Chang. His expertise aligns closely with the lab's development, covering AIoT, Medical IoT, and Industrial IoT. The lab values not only algorithm research, but also hardware-software integration and practical deployment.
Professor Chang's advising style balances direction-setting and hands-on implementation. He provides clear research directions and strong resources while giving students flexibility to explore. He values practical ability and deployable outcomes, and is open to discussion.
The lab atmosphere is focused but not oppressive. We value teamwork and knowledge transfer. Senior and junior students support one another technically and solve problems together, creating a collaborative and warm learning environment.
The lab is located on the 8th floor of the Information Industry Building at the NKUST Jianguo Campus.
FAQ Category

Admissions and Joining

4 frequently asked questions

Yes. We welcome motivated doctoral students, master's students, and undergraduate project students. We also welcome international students and aim to provide an international research environment.
Yes. We care more about your willingness to learn and your initiative. If you have basic programming logic, the lab can provide training and support from senior students to help you steadily enter the field.
Python is the main language for AI development in the lab. Familiarity with Python helps, and C/C++ or other languages are also useful, especially for IoT and edge-device development. These are not strict requirements as long as you are willing to learn.
No. You do not need to be an AI expert before joining. We can guide you from basic algorithm concepts to hands-on implementation. What matters most is being willing to face challenges and solve problems.
FAQ Category

Undergraduate Projects

17 frequently asked questions

Project students can work with frontier technologies such as AI visual recognition, healthcare AI system development, and IoT edge-device implementation. We strongly encourage students to turn research ideas into demonstrable prototype systems, which can strengthen graduate-school applications and job interviews.
Both are possible. If you do not have a clear direction yet, the professor and senior students can provide potential topics. If you already have an idea, you are welcome to discuss it with the professor, and we can help turn it into a feasible and meaningful project.
The difficulty is adjusted based on each student's background. The goal is to help you grow within a reasonable challenge level. If you get stuck, senior students will support you, so you will not be left alone.
We follow the principle that coursework comes first. With proper time management and steady progress, most students can balance academic courses and project work. Exam periods can be handled with flexible scheduling.
No need to worry. We value learning attitude more than starting point. The lab has beginner training and senior-student support, so even students starting from zero can gradually build confidence and skills.
We encourage projects to go beyond reports and become demonstrable prototype systems. These may include complete functions, user interfaces, and application scenarios, giving your work practical value.
Yes. A complete project system, GitHub portfolio, and demo presentation can be strong evidence in graduate-school applications and job interviews.
Our projects are system-implementation oriented. Instead of only writing reports, students work through data processing, model design, and front-end/back-end integration to build systems that truly run.
Yes. We encourage and support students to join competitions or academic presentations when project outcomes are strong enough, helping students build experience and resume highlights.
You will not be left on your own. In addition to the professor's guidance, senior students provide technical support and share experience so you can avoid many unnecessary detours.
Projects are usually team-based, similar to real industry development. You will learn not only programming, but also task division, system integration, and problem solving with teammates.
It depends on the project stage, but around 6 to 12 hours per week is usually enough for steady progress. The key is consistent accumulation rather than short bursts of intense work.
Yes, if you plan your time well. Most students can balance coursework, project work, and part-time jobs, and the lab can provide flexible arrangements when needed.
There may be opportunities through industry-academia collaboration, such as medical images or production-line data, making projects closer to real applications.
Potentially yes. We encourage students to deepen projects into applicable systems. If a project has commercial value, it may be extended toward productization or entrepreneurship.
We provide ongoing technical support and direction adjustment during the process. The goal is not instant success, but learning how to solve problems step by step.
It includes more than models. We emphasize the full pipeline from AI models to system integration, including data processing, training, deployment, and application demos.
FAQ Category

Master's Students

15 frequently asked questions

We strongly encourage students to submit strong research outcomes to domestic or international conferences and journals, which is valuable for future careers. The professor also adjusts expectations according to each student's career goals, such as entering industry or continuing to doctoral study.
Most students who maintain normal research progress and report regularly in meetings can graduate within the standard period of about two years. The lab does not intentionally hold students back.
The lab uses a responsibility-based and flexible-time approach. There is no strict clock-in requirement. We value research progress and output quality more than the number of hours spent at a desk.
There will be a technical adjustment period because this is a hands-on lab. However, the lab has accumulated codebases and learning resources, and senior students and the professor provide code reviews and guidance. If you face the challenge directly, you can get through it.
The professor's style is based on trust and empowerment. He provides key guidance and direction, and adjusts the level of follow-up based on each student's self-discipline. Students are given room to develop independently.
It depends on progress and ability, but most students focus on one or two major research or industry-academia projects. We encourage doing one topic deeply rather than spreading effort across too many projects.
Both are possible. The professor provides promising research directions and topics, and students with their own ideas are also welcome to discuss them. We help adjust ideas into feasible and meaningful research topics.
Meetings are usually weekly or biweekly, adjusted according to research progress. Regular discussion helps keep the direction correct and the work moving forward.
In many cases there is collaboration and exchange. Different projects often share technologies and resources, and students may cooperate in system development or project tasks.
Yes. The lab has a flexible scholarship system based on student ability and performance. Students who are willing to contribute and show outcomes can usually receive appropriate support.
Yes. The lab collaborates with companies and hospitals, giving master's students opportunities to work on practical projects, real needs, and field data.
Yes. When research outcomes reach a suitable level, the lab encourages submissions to international conferences and provides related resources and funding support.
Many graduates enter the technology industry, including semiconductor, AI, and startup companies, as software, algorithm, or system engineers. Some students continue to doctoral study.
Yes. The professor helps students plan research directions, publications, and application strategies, and provides recommendations and resources for doctoral applications.
The lab has a complete research support system, including senior students, postdoctoral researchers, and direct professor guidance. Dedicated staff also handle administrative work so students can focus on research and quickly build practical skills.
FAQ Category

PhD Students

12 frequently asked questions

Yes. PhD students are treated as independent researchers with high autonomy. The professor discusses advanced technologies with students as a collaborator and supports each student in developing a distinctive research direction and long-term academic plan.
Yes, strongly. For high-quality international journals and flagship conferences, the lab provides academic resources and funding support to help PhD students reach the international stage.
Yes. Depending on current projects and research topics, PhD students may collaborate with overseas universities or multinational companies, and may pursue short-term international exchange opportunities.
Students must meet the doctoral program's publication-point requirements at NKUST. The lab does not add unnecessary extra restrictions and helps students plan publications according to research progress and quality.
The lab mainly targets international journals and academic conferences in AI, healthcare AI, and smart manufacturing, and encourages students to challenge influential journals and representative conferences.
Yes. The lab has accumulated publications in international journals and conferences, including influential journals and high-quality venues, and has experience with submission and review responses.
Yes. PhD students usually lead their research and have opportunities to serve as first authors, taking responsibility for the core research and writing.
Yes. The professor and team provide guidance on paper structure, writing, and submission strategy to improve paper quality and acceptance potential.
Yes. Full-time PhD students may receive scholarships or related funding support so they can focus on research and academic development.
Yes. When research outcomes meet submission standards, the lab encourages international conference participation and provides related funding support to broaden international exposure.
Yes. The lab has postdoctoral researchers and a complete research team that provide technical support, experience transfer, and a research discussion environment.
Graduates can enter academia, research institutions, or industry positions in AI, semiconductors, and system development, with strong potential and competitiveness.
FAQ Category

Industry-Academia and Career Development

3 frequently asked questions

Yes. This is one of our strongest advantages. The lab connects with many hospitals and well-known companies. Students do not only work on simulated data; they participate in real industry projects and develop systems that can be used in practice.
Yes. Through industry-academia collaboration, students may access valuable real-world data such as clinical images or production-line sensing data, making their research closely connected to industry needs.
Career prospects are strong. Many graduates enter leading technology companies, system companies, semiconductor-related industries, or AI startups as algorithm, software, or firmware engineers. Some students continue doctoral study in Taiwan or abroad.
FAQ Category

Technologies and Learning

3 frequently asked questions

The technical stack is broad and practical, including Python and C/C++ development, PyTorch, computer vision such as YOLO and image processing, IoT sensing data collection, Edge AI deployment, and front-end/back-end system integration.
Yes. We have a mature onboarding system that includes teaching documents passed down from previous students, hands-on practice tasks, and one-on-one mentoring from senior students.
Yes, if you have determination to solve problems and courage to make mistakes. Many strong students started from zero. The key is being willing to practice, ask questions, and learn actively.
FAQ Category

Facilities and Resources

2 frequently asked questions

Yes. To support AI computation, the lab has high-end GPU servers equipped with RTX 3090 and 4090 cards. For edge devices, students can use NVIDIA Jetson devices, ToF depth sensors, thermal cameras, and various IoT modules for implementation and testing.
Yes. When your paper is accepted by a quality conference or journal, the lab and university usually provide support such as registration, travel, and accommodation subsidies to encourage international exposure.
FAQ Category

Other Common Questions

15 frequently asked questions

Project deadlines or paper submission periods can be busy, but we strongly oppose ineffective long overtime. If you work efficiently and maintain progress, you can still keep a healthy balance.
The lab does not intentionally hold students back. If you complete the professor's required progress and graduation requirements, most students can graduate on time.
Yes. The lab is flexible. If you discover another topic that better fits your interests after initial exploration, you can discuss it with the professor, who will evaluate feasibility and help you transition smoothly.
Yes. The lab occasionally holds midterm or end-of-semester meals, welcome and farewell gatherings, and sports or social activities, balancing research with team connection and well-being.
There will be challenges, but the lab provides team support and senior-student help. The goal is to help you grow within a manageable level of pressure.
No. The overall atmosphere is relaxed and collaboration-oriented. Members help one another, solve problems together, and also participate in social activities.
No. We use flexible time management and value outcomes rather than seat time. Graduate students do have their own desks for research and discussion.
No. The lab has senior students, postdoctoral researchers, and team support. Members are encouraged to discuss and help one another.
No. We value learning attitude and problem-solving ability. If you are willing to learn, the lab provides resources and guidance to help you grow.
No. We value student participation and growth. You will take part in system development and problem solving, building abilities and experience that belong to you.
Yes. We emphasize hands-on implementation and system integration, including AI models, system development, and real applications, all of which are highly demanded in industry.
It is suitable for people who are willing to learn, take initiative, solve problems, and are interested in technology. If you want real technical growth and practical experience, this lab can be a good fit.
Students who are unwilling to invest time in learning or who prefer to wait passively for assigned tasks may not be a good fit. We value initiative and willingness to grow.
It is possible, but not strongly recommended because you may miss many growth opportunities. The lab is best suited for students who want to build technical ability and practical experience.
Very suitable. We emphasize hands-on system development, allowing students to build from fundamentals toward practical capability for future careers or advanced research.
FAQ Category

Industry-Academia Collaboration and External Services

10 frequently asked questions

Yes. We have long-term collaborations with hospitals and companies in healthcare AI, AI image recognition, smart manufacturing, and IoT system integration, helping research outcomes reach real-world deployment.
We can help develop applications such as image recognition, defect detection, medical image analysis, behavior recognition, sensing data analytics, smart monitoring systems, and AI edge-computing deployment.
Yes. We have collaborated with multiple medical institutions and companies on clinical assistance systems, smart monitoring systems, and AI detection applications for manufacturing, with complete practical experience.
Common models include industry-academia projects, commissioned technology development, system validation testing, and joint applications for government programs such as NSTC or industry-academia funding.
Yes. We have experience handling real field data and value privacy and security. For medical applications, we can work with IRB processes for research and development.
No. Collaboration can start from problem discussion and requirement analysis. We can help evaluate feasibility and propose suitable technical approaches.
Yes. We are experienced in building prototype systems and proof-of-concept validation from zero to one, helping partners quickly evaluate technical feasibility and business value.
Yes. Our goal is not research alone, but practical deployment, including system deployment, model optimization, and application integration.
Partners can gain AI technology adoption, process optimization, cost reduction, and efficiency improvement, while also strengthening technical competitiveness and innovation through industry-academia collaboration.
You are welcome to use the website contact information or contact the professor and lab directly. We can arrange further requirement discussions and collaboration planning.