M.S. in Quantitative Biology and Bioinformatics
The study of Biology is undergoing a revolution driven by new technologies that enable scientists to generate extensive amounts of data. For example, the costs of sequencing nucleic acids have dropped dramatically, resulting in unprecedented amounts of genomic, transcriptomic, and proteomic data. Advances in imaging extend from the nano to the macro scale to probe function and generate enormous amounts of data that describe behaviours of cells from subcellular to organ-levels. The new datasets cut across all subdisciplines in biology and enable scientists to ask questions in new ways to reveal the fundamental rules of life.
The M.S. in Quantitative Biology and Bioinformatics (MS-QBB) will prepare students for new careers bioinformatics and related fields. Our mission is to provide students who have background in life sciences skills to prepare for careers in bioinformatics. This program allows student to choose a 2-semester or a 3-semester program of study. If you are interested in applying, learn more about the application process on our admissions page or e-mail us.
Our 2-semester option allows students to quickly gain the most relevant skills in bioinformatics. Students will begin study in late August and graduate in late May.
The 3-semester option allows students to spend a third semester gaining additional experience and some more advanced coursework. Students will begin study in late August, have the option to earn course credit with summer internships (interested students may apply to these in the first year), then students will complete their third semester in the following Fall and graduate in late December.
Students are encouraged to seek external internships after their first year and pursue this degree full-time, completing the program in 3 semesters.
Students who are interested in this program may also want to consider the M.S. in Computational Biology and M.S. in Automated Science programs. Those programs expect a higher level of quantitative background & skills to enter and are designed to engage students with a more in-depth focus computational machine learning competencies and the application of machine learning to biological research.
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