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MPhil in Computational Biology

The MPhil in Computational Biology course is aimed at introducing students in the biological, mathematical and physical sciences to quantitative aspects of modern biology and medicine, including bioinformatics. The course was developed by the Cambridge Computational Biology Institute and is run by the Department of Applied Mathematics and Theoretical Physics.

Course Overview

The course is intended for mathematicians, computer scientists and others wishing to learn about the subject in preparation for a PhD course or a career in industry. It is also suitable for students with a first degree in biosciences as long as they have strong quantitative skills.

Core Modules

Core modules include:

• Bioinformatics
• Scientific programming with R
• Genomics All students joining this course are required to attend an introductory course in molecular biology.

Learning Outcomes

After completing the MPhil in Computational Biology, students will be expected to have:

• Acquired a sound knowledge of a range of tools and methods in computational biology
• Developed the capacity for independent study and problem-solving at a higher level
• Undertaken a research project within a laboratory or group environment, and produced a project report
• Given at least one presentation on their project

Course Structure

The course concludes with a three-month research project in a university or industrial laboratory. Courses are delivered in association with several departments from across the University and neighbouring institutes.

Key Information

• Duration: 10 months full-time
• Study Mode: Taught
• Degree: Master of Philosophy
• Department: Department of Applied Mathematics and Theoretical Physics

Admission Criteria

Students with a first degree in biosciences are required to have strong quantitative skills, which should be documented in the application.

Funding and Deadlines

• Applications open: September 3, 2025
• Application deadline: February 3, 2026
• Course starts: October 1, 2026
• Funding deadline: January 7, 2026

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