Welcome to the MIT Computational and Systems Biology PhD Program (CSB)

The program seeks to train a new breed of quantitative biologists who can take advantage of technologies at the leading edge of science and engineering to tackle fundamental and applied problems in biology. Our students acquire: (i) a background in modern molecular/cell biology; (ii) a foundation in quantitative/engineering disciplines to enable them to create new technologies as well as apply existing methods; and (iii) exposure to subjects emphasizing application of quantitative approaches to biological problems.  Our program and courses emphasize the logic of scientific discovery rather than mastering a specific set of skills or facts.  The program includes teaching experience during one semester of the second year.  It prepares students with the tools needed to succeed in a variety of academic and non-academic careers.

The program is highly selective with typical class sizes 8 to 10 students. About half of our graduate students are women, about one quarter are international students, and about 10% are under-represented minorities.

Students complete most coursework during the first year, while exploring research opportunities through 1- or 2-month research rotations.  A faculty academic advisor assigned in the first year provides guidance and advice. Students choose a research advisor in spring or early summer of year 1 and develop a Ph.D. research project in with their advisor and input from a thesis committee chosen by the student.

Average time to graduation is 5½ years. 

The Program in CSB is committed to increasing opportunities for under-represented minority graduate students and students who have experienced financial hardship or disability.

Latest News:

Seeking the cellular mechanisms of disease, with help from machine learning

April 6, 2021

 

Caroline Uhler blends machine learning, statistics, and biology to understand how our bodies respond to illness

Image: Adam Glanzman

Caroline Uhler’s research blends machine learning and statistics with biology to better understand gene regulation, health, and disease....

Read More