Msc Computational Methods In Ecology And Evolution in Imperial College London, UK

Msc Computational Methods In Ecology And Evolution in Imperial College London

In this unique course we teach quantitative methods and biological concepts together, through application of the methods to cutting-edge biological research problems.

The focus is on current topics in modern quantitative biology, such as interactions between ecological and evolutionary dynamics, and the effects of climate change on ecological communities.

You will also have the chance to develop your knowledge in mathematical, statistical, and computing tools through their application to important research problems.

Over the past 10–20 years, biology has become increasingly quantitative, and mathematical sciences have in turn been increasingly influenced by biology.

It has been said that “mathematics is biology's next microscope, only better” (Cohen, Plos Biology, 2004) because mathematical, statistical, and computational sciences will continue to reveal unsuspected and entirely new worlds within biology, just as the microscope revealed previously unseen worlds following its invention.

It has also been said that “biology is mathematics' next physics, only better” (Cohen, Plos Biology) because biology will in turn continue to spur major new developments in computation, mathematics and statistics, just as physics has done in past centuries.

This course is suitable for:

• Life scientists wishing to expand their quantitative skills in light of the increasingly quantitative nature of modern biology
• Physical scientists (mathematicians, physicists, statisticians, computer scientists) with a strong interest in biology

Study programme

You take approximately half of your core modules in the Autumn term before completing the remainder in the Spring term.

You begin your research project in the Summer term which accounts for half of your MSc grade.

Careers

The course serves as ideal preparation for either PhD studies or employment in fields of applied quantitative biology, such as resource management and conservation.

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