QS ranking profile
Inha University
This page presents clearly labeled QS ranking data for orientation. It does not verify programs, supervisors, funding, admission requirements, or the university's website.
- QS 2026 rank
- #643
- Down 12 places since 2025
- QS 2025 rank
- #631
- Overall score
- 26.9
Explore opportunities at Inha University
Browse active research positions, doctoral fellowships, and university funding calls indexed from official faculties and verified sources.
QS indicators
Reported ranking indicators
Each indicator is scored out of 100 in the same QS snapshot as the summary above.
#699 worldwide
#659 worldwide
#198 worldwide
#661 worldwide
#752 worldwide
#650 worldwide
#622 worldwide
#801 worldwide
#779 worldwide
#405 worldwide
Institution profile
How QS classifies this institution
QS groups institutions by size, subject range, and research output.
- Size
- Large
- About 12,000 to 30,000 students
- Subject range
- Focused
- Teaches a limited range of faculty areas
- Research intensity
- Very high
- Very high research output in the QS classification
- Region
- Asia
- QS regional grouping
University profile
About Inha University
Editorial background written for orientation. Verify programs, funding, and admission on the university's official pages.
Inha University combines engineering with science, business, and the humanities
Inha University in Incheon presents a broad academic structure that grew from an engineering tradition. Its public college and major information lists engineering fields such as mechanical, aerospace, naval architecture and ocean engineering, industrial, chemical, biological, polymer, materials, civil, environmental, architectural, energy resources, electrical, electronic, computer, and information and communication engineering. The same directory includes natural sciences, business administration, the Asia Pacific School of Logistics, social sciences, humanities, arts and sports, and related professional fields. This range gives an applicant several possible homes for a STEM project, but the department page defines the actual route.
The subject map is especially useful for questions that cross technology and society. Logistics can connect engineering, trade, management, ports, and transport. Computer and information communication fields can connect hardware, software, networks, and data. Natural-science departments include mathematics, statistics, physics, chemistry, biological sciences, ocean sciences, and food and nutrition. The college names are a starting point for navigation rather than a statement that every programme is taught in English or that every department offers the same research pathway.
Inha research links artificial intelligence with industry and health
The university's AI Convergence Research Center and Graduate School describe an industry-facing model that connects AI with manufacturing, logistics, ports and airports, and medical applications. The centre groups work around perception, logistics and spatiotemporal analysis, time-series prediction, and interpretation and prediction of medical data. Its materials describe an AI convergence major in the College of Engineering and an AI major in Electrical and Computer Engineering, with graduate study designed around collaboration with partner companies. These details are useful for identifying the kind of problem a proposed project should address, while the department and supervisor pages remain the place to check the current academic setting.
The research direction can be read alongside Inha's wider engineering and science structure. A manufacturing question may require materials, industrial engineering, mechanical systems, or computer vision. A logistics question may involve optimisation, information systems, transportation, or operations. A medical-data project may need a clinical partner, a privacy plan, and a method that matches the available data. The AI label alone is not enough. An applicant should name the input, model, physical setting, and form of evaluation before comparing a research group.
How to assess an Inha University study or research option
Start with the research object and decide what kind of evidence would answer the question. For an engineering project, record the device, material, system, or simulation. For computer science, specify the data, algorithm, software environment, or security problem. For logistics, write down the network, facility, process, or decision being studied. A natural-science project may need a laboratory or field sample, while a business or social-science project may examine an organisation, market, or policy. These details help you choose a department instead of relying on a general university label.
This page offers institutional background on Inha University; it is not a judgement on one college, programme, laboratory, or supervisor. When you save the option, write down the exact programme or department, research question, approach, study language, and a live Inha University page describing the route. Keep the AI Convergence Center's industry themes separate from evidence about one available project. For each option, specify whether the work uses a laboratory, computational model, clinical setting, industrial partner, field site, or organisation, because each setting requires different preparation and supervision. Record the expected output and the kind of partner or facility the project needs. That makes a broad STEM interest into a route you can compare carefully.