| Course Level: | Doctorate | |
|---|---|---|
| Course Duration: | 2 Years | |
| Course Language | English | |
| Required Degree | Master’s Degree | |
| First Year Total Fees: | $ 30000(₹ 2500000) | |
| Total Course Fees: | $ 60000(₹ 5000000) | |
Choosing a PhD in Machine Learning at Hallym University, South Korea, offers a unique blend of advanced research, industry collaboration, and global exposure. Hallym’s cutting-edge labs focus on AI, deep learning, and data-driven innovation, guided by experienced faculty actively publishing in top-tier journals. The university fosters interdisciplinary projects, connecting machine learning with healthcare, robotics, and smart technologies, providing practical impact. South Korea’s vibrant tech ecosystem offers excellent networking and career opportunities. Small class sizes ensure personalized mentorship, while state-of-the-art facilities support innovative experiments. Hallym also emphasizes international collaboration, giving students a competitive edge in academia and industry worldwide.
Category |
Details |
|---|---|
|
Program Name |
PhD in Machine Learning |
|
Degree Awarded |
Doctor of Philosophy (PhD) |
|
Course Duration |
3–5 years (full-time) |
|
Language of Instruction |
English / Korean (depending on course) |
|
Yearly Tuition Fees |
Approx. KRW 4,000,000 – 6,000,000 (~USD 3,000–4,500) |
|
Total Tuition Fees |
Approx. KRW 12,000,000 – 30,000,000 (~USD 9,000–22,500) |
|
Total Program Cost |
Tuition + Living Expenses: ~KRW 30,000,000 – 50,000,000 (~USD 22,500–37,500) |
|
Eligibility |
Master’s degree in Computer Science, AI, Data Science, or related field |
|
Admission Requirements |
Academic transcripts, recommendation letters, research proposal, CV, English proficiency (TOEFL/IELTS) |
|
Application Intake |
Usually Fall (September); check official website for Spring intake |
|
Scholarship |
University scholarships, Korean Government Scholarship Program (KGSP), research assistantships |
|
Career Prospects |
AI researcher, Data Scientist, Machine Learning Engineer, Academic faculty, R&D roles in tech companies |
|
Curriculum Structure |
Core ML courses, Advanced AI topics, Research Seminars, Thesis Research, Electives in Data Mining, Deep Learning, Computer Vision, NLP |
