MSc Mathematical Biology

A  2.1 undergraduate Honours degree in Mathematics, Statistics or a closely related subject area. If you studied your first degree outside the UK, see the international entry requirements.

The qualifications listed are indicative minimum requirements for entry. Some academic Schools will ask applicants to achieve significantly higher marks than the minimum. Obtaining the listed entry requirements will not guarantee you a place, as the University considers all aspects of every application including, where applicable, the writing sample, personal statement, and supporting documents.

Application requirements

• CV 
• personal statement (optional)
• two original signed academic references 
• academic transcripts and degree certificates.

For more guidance, see supporting documents and references for postgraduate taught programmes.

For fees and funding options, please visit website to find out more

The skills obtained during this MSc programme are highly valued for a PhD and further academic career in mathematical biology as well as for a professional research scientist career in the private sector.

These skills are sought after, among others, in advanced Research and Development positions in: 

• pharmaceutical companies 
• medical devices companies 
• life-sciences industry 

Recent Masters and Doctoral students in the School of Mathematics and Statistics have been employed at some of the best universities and most well-known companies around the world. 

The Careers Centre offers one-to-one advice to all students as well as a programme of events to assist students in building their employability skills.

Compulsory

• Spatial Models and Pattern Formation in Mathematical Biology: explores applications of partial differential equation to real world biological and medical problems, e.g., cell migration, pattern formation in animal coat markings, spread of infectious diseases, and more. 
• Stochastic Dynamics in Biology: provides an introduction to stochastic mathematical modelling with a focus on applications in biology, such as gene regulation, and the dynamics of cell- or animal populations. 
• Mathematical Oncology: using a wide range of methods and techniques we develop and study mathematical models addressing biomedical questions in cancer growth, metastasis and treatment. 
• Advanced Computational Techniques: studies several ideas and techniques that underpin modern numerical treatment of processes described by partial differential equations. 

Semester One - Optional

Students choose two optional modules from this list: 

• Applied Statistical Modelling using GLMs: covers the main aspects of linear models and generalised linear models, including model specification, various options for model selection, model assessment and tools for diagnosing model faults. 
• Calculus of Variations in Biological Modelling: using variational techniques as a main tool, we develop mathematical models for various biological phenomena including cell migration, protein structure, and limb regeneration. 
• Computational Numerical Analysis:  introduces programming for the implementation of mathematical algorithms to solve a wide range of mathematical problems. 
• Introductory Data Analysis: covers essential statistical concepts and analysis methods relevant for commercial analysis. 
• Modelling Wildlife Population Dynamics: introduces students to methods for constructing mathematical models of wildlife population dynamics and of fitting these models to diverse data from wildlife surveys. 

Optional modules are subject to change each year and require a minimum number of participants to be offered; some may only allow limited numbers of students (see the University's position on curriculum development). 

Semester Two - Optional

Students choose one optional module from this list: 

• Advanced Analytical Techniques: introduces important advanced applied analytic techniques such as variational calculus, integral equations, solutions to differential equations by contour integrals, and the theory of steepest descent.
• Estimating Animal Abundance and Biodiversity: introduces the main types of survey methods for wildlife populations.    
• Fluid Dynamics: introduces the theory of incompressible fluid dynamics, with particular attention to conservation laws. 
• Medical Statistics: covers a number of key topics in the field that are important for both methodological development and application.  
• Population Dynamics Models in Mathematical Biology: explores applications of difference and ordinary differential equations mathematics to real world biological problems e.g., harvesting of fish stocks, host-parasitoid systems, predator-prey dynamics, and more. 

Optional modules are subject to change each year and require a minimum number of participants to be offered; some may only allow limited numbers of students (see the University's position on curriculum development). 

Dissertation

MSc students complete a dissertation during the final three months of the course to be submitted near the end of August. Dissertations are supervised by members of teaching staff who will advise on the choice of subject and provide guidance throughout the progress of the dissertation.  

If students choose not to complete the dissertation requirement for the MSc, there is an exit award available that allows suitably qualified candidates to receive a Postgraduate Diploma. By choosing an exit award, you will finish your degree at the end of the second semester of study and receive a PGDip instead of an MSc.