Work on learning that holds up in the real world
We are looking for students who want to build efficient, adaptive, and trustworthy learning algorithms — and see them matter beyond the benchmark.
Ph.D. students
Full-time doctoral research in any of the lab's areas. Admission through the Graduate School of Data Science.
M.S. students
Thesis-track master's research, typically leading to a first-author publication.
Undergraduate interns
Research internships for undergraduates with strong ML foundations, usually one semester or a summer.
One email is enough
We read every application. A short, specific email tells us more than a long generic one.
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01
Email the PI
Send your CV and transcript to pi-email@snu.ac.kr with the subject "[Join] your name — position".
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02
Tell us what you want to work on
A paragraph on which research area interests you and why. Pointers to code, projects, or papers help.
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03
Interview
A conversation about your background and interests, plus a short technical discussion of a paper you like.
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04
Apply to GSDS
Graduate positions require admission to the Graduate School of Data Science; we will guide you on timing.
Strong foundations
Comfort with linear algebra, probability, and optimization — and the ability to turn them into working code.
Curiosity with follow-through
You ask why a method works, run the experiment, and keep going when the first answer is wrong.
Care for real impact
Interest in whether methods hold up outside the benchmark — in language, physical, or industrial systems.