Zee-Fen Chang

Hsiu-Hsiang Lee

Ya-Wen Liu

Hsin-Yue Tsai

The Institute of Molecular Medicine

Established in 1992, the Institute of Molecular Medicine (IMM) is an interdisciplinary research unit dedicated to mechanistic studies of important biomedical topics. Located at both the College of Medicine, National Taiwan University (NTU) and NTU Hospital, IMM has convenient access to the most advanced research facilities at the NTU medical campus.

Read More

Curriculum

To train future generations of manpower for innovative biomedical research, IMM offers master and PhD degree programs that emphasize logic thinking and problem-solving, as well as the ability to present and discuss in English. In addition to the required course of molecular cell biology, regular journal clubs and public progress report are conducted in English.

Read More

Research

IMM is dedicated to mechanistic studies of basic biological processes as well as disease-related topics. This includes, but is not limited to, gene expression and function, organism development, nucleic acid biology, immunity, vesicular trafficking and membrane dynamics, neuroscience, behavior and stress response.

Read More

Students and Alumni

IMM recruits motivated students with talent from Taiwan and other countries. Thanks to NTU, MOST and MOE, PhD students at IMM are financially supported up to 15000 USD/year. Master students also receive financial support that eases the living cost in Taipei. All students are eligible for applying for numerous scholarships available at NTU and other foundations.

Read More

Faculty

More

Fang-Jen Lee

Distinguished Professor

Regulation of Protein Trafficking

Zee-Fen Chang

Chair Professor

Nucleotide metabolism in DNA damage

Chun-Liang Pan

Distinguished Professor and Director

Developmental Neurobiology

Li-Chung Hsu

Professor

Molecular Immunity and Signal Pathway

Hsiu-Hsiang Lee

Associate Professor

Neuronal Development

Ya-Wen Liu

Professor

membrane dynamics

Hsin-Yue Tsai

Associate Professor

RNA biochemistry, Next Generational Sequencing data analysis