Faculty Position in School of Electrical Engineering, KAIST
“AI and Machine Learning”
The School of Electrical Engineering at KAIST https://ee.kaist.ac.kr/ invites applications for tenure and tenure-track faculty appointments at the Assistant, Associate, and Full Professor level in:
All areas related to AI and Machine Learning with applications including, but not limited to, computer vision, natural language processing, signal processing and communications/networks, data mining, expert systems, security, cloud, data science, neuromorphic computing, robotics, manufacturing systems, automotive, embedded systems, AI architecture and circuits, semiconductor devices for AI.
Priority, however, will be given to the overall originality and promise of the candidate’s work over any specific area of specialization.
Applicants must have Ph.D. in a closely related field before the point of appointment, and some post-doctoral research experience is desirable.
A successful candidate will be expected to teach courses at the graduate and undergraduate levels and to build and lead a team of graduate students in Ph.D. research. Financial and in-kind resources are available to pursue activities that help accelerate our efforts to achieve our equity and inclusion goals, with the full backing of the College.
Applications should include a brief research and teaching plan, a detailed resume including a publications list, and the names and email addresses of at least three references. Additional recommendation letters may be solicited by the school for tenured-level finalists during the final stage of the recruitment process. Candidates may also submit an optional cover letter.
The review of applications will begin on June 14, 2019, and applicants are strongly encouraged to submit complete applications by June 12, 2019 for full consideration.
KAIST is an equal employment opportunity and affirmative action employer. Women and non-Koreans are especially encouraged to apply. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, protected veteran status, or any other characteristic protected by law.