Last update: April. 02, 2025.

NOTICE! - MS, Ph.D. positions Opened

  The Applied Intelligence & Robotics(AIR) Lab. invites applications for fully-funded MS, Ph.D. positions (starting 2025) in the areas of sensor fusion, deep learning, SLAM, and autonomous navigation. The positions are open for motivated candidates with a background in Mechanical Engineering, Robotics, Computer Science or Mechatronics. It is expected that mechanical design, computer science, mobile robotics, machine learning, and optimization, as well as analytical/experimental mechanics, will be key features of the research. Candidates with a broad range of technical skills and a track record in translating conceptual ideas into working prototypes will be strongly considered. Evidence of an ability to work in collaborative teams and good communication skills (oral and written) is essential. In addition, successful candidates will be expected to publish scholarly papers and attend international conferences. Please contact Dr. Sejin Lee, (sejiny3@kongju.ac.kr), and attach your CV, and cover letter describing research interests.

  The Applied Intelligence & Robotics Laboratory(AIR Lab.) was established in 2013 when Dr. Sejin Lee joined the Division of Mechanical and Automotive Engineering, College of Engineering at Cheonan, Kongju National University. The AIR Lab researches many aspects of robot machine autonomy in sensor fusion. 

Welcome to our homepage to all of you!

응용지능로봇(AIR) 연구실에서는 센서 융합, 딥러닝, SLAM 및 자율 내비게이션 분야에서 2025년에 시작되는 석사, 박사 과정을 위한 지원자를 모집합니다. 기계공학, 로보틱스, 컴퓨터 과학 또는 메카트로닉스 배경을 가진 동기 부여된 지원자에게 열려 있습니다. 연구는 기계 설계, 컴퓨터 공학, 이동 로봇, 머신러닝, 최적화뿐만 아니라 분석 및 실험이 중요한 특징이 될 것입니다. 개념적 아이디어를 실용적인 프로토타입으로 구현한 경험이 있는 다양한 기술 역량을 가진 지원자는 우대됩니다. 또한 팀에서 협업으로 일할 수 있는 능력과 우수한 의사소통 능력(구두 및 서면)은 필수입니다. 연구 관심사를 설명하는 자기소개서와 이력서를 Dr. Sejin Lee(sejiny3@kongju.ac.kr)에게 보내주시기 바랍니다.

응용지능로봇연구실(AIR Lab)은 2013년 이세진 교수가 천안에 위치한 공주대학교 공과대학 기계자동차공학부에 합류하면서 설립되었습니다. AIR Lab은 센서 융합을 통한 로봇 기계 자율성의 여러 측면을 연구합니다.

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Current Research Interests

  The environmental description based on robotic sensing is fundamentally essential to be applied to the autonomous navigation for mapping, localization, simultaneous localization and mapping, obstacle avoidance, path planning, exploration, and decision making. We are trying to handle these issues with the deep learning approach which is a branch of machine learning based on a set of algorithms that attempt to model high-level abstractions in data by using multiple processing layers with complex structures or otherwise composed of multiple non-linear transformations. The unmanned vehicles can be expected to do their navigational tasks in swarms with a sort of artificial intelligence on the ground, water surface, aerial space, and so on.