KAIST

< (Back row from left) Prof. Yoon Ki Kim, Prof. Seung-Jae V. Lee, and Gwangrog Lee; (Front row from left) Dr. Sung Ho Boo, Sieun S. Kim, Seokjin Ham, and (top) Donghun Lee > Cells in our bodies produce RNA based on genetic information stored in DNA, and RNA serves as a blueprint for making proteins. Researchers at our university have discovered a new phenomenon: removing 'circular RNA' that accumulates in cells as we age can slow down aging and extend lifespan. This study provides cru

<(From left) Professor Sang Wan Lee, Myoung Hoon Ha, and Dr. Yoondo Sung> Artificial intelligence now plays Go, paints pictures, and even converses like a human. However, there remains a decisive difference: AI requires far more electricity than the human brain to operate. Scientists have long asked the question, “How can the brain learn so intelligently using so little energy?” KAIST researchers have moved one step closer to the answer. KAIST (President Kwang Hyung Lee) an

<(From left) KAIST Ph.D. Candidate HyunWoo Chang, Professor EunAe Cho. (Top, from left) Seoul National University Professor Won Bo Lee, Dr. Jae Hyun Ryu.> In the era of climate crisis, hydrogen vehicles are emerging as an alternative for eco-friendly mobility. However, the fuel cell, known as the ‘heart of the hydrogen car,’ still faces limitations of high cost and short lifespan. The core cause is the platinum catalyst. While it is a decisive material for generating electri

< (From left) Dr. Ju-Gyeong Kang, Ph.D candidate TaeJun Seol, Professor Dae-Sik Lim > Metabolic diseases such as obesity, fatty liver, and insulin resistance are rapidly increasing worldwide, but fundamental methods to regulate the process of fat formation remain limited. In particular, once adipocytes (fat cells) are formed, they are difficult to reduce, making treatment challenging. Amidst this, a research team from our university has discovered the existence of a ‘switch&rsquo

<(From Left) Professor Byungha Shin, Ph.D candidate Chaeyoun Kim, Dr. Oki Gunawan> Semiconductors are used in devices such as memory chips and solar cells, and within them may exist invisible defects that interfere with electrical flow. A joint research team has developed a new analysis method that can detect these “hidden defects” (electronic traps) with approximately 1,000 times higher sensitivity than existing techniques. The technology is expected to improve semiconducto