Masters (MSc or MRes) in Computational Methods in Ecology and Evolution (CMEE)
Autumn term 2020-21
Courses will begin on schedule in Autumn and we look forward to seeing new and returning students in person, if travel and visa arrangements allow. If students can’t travel to campus in time for the start of term, the Department has made plans to still provide them a high-quality remote educational experience during the Autumn term.
Teaching will be a combination of on-campus (in-person) and remote learning (online), known as ‘multi-mode’ delivery. For more information about multi-mode delivery, the learning experience and the steps we’ll be taking to keep students safe on campus, please see our Covid-19 information for applicants and offer holders page.
We will update our course pages with further details once they are finalised.
This course is for students with a passion for biology, who wish to be trained in cuttng-edge quantitative techniques in ecology, evolution and conservation.
- Quantitative skills are a critical limiting factor in modern biology, including the fields of Ecology, Evolution, Behavior and Conservation Biology
- In this course students learn those skills in application to important biological problems at one of the world's leading institutions for quantitative biology
- Students leave with skills in computing, statistics, and mathematical modelling that give them a major edge in competing for PhDs and jobs
- The course is suitable for students from life, physical, computer as well as mathematical sciences backgrounds
- Computational tools are biology's next microscope, only better - learn to use them now!
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, J.E., 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, J.E., PloS Biology, 2004) because biology will in turn continue to spur major new developments in computation, mathematics and statistics, just as physics has done. In this unique course, we teach quantitative (with focus on computing) methods and biological concepts together, through application of the methods to contemporary biological research problems.
Key features of the course:
- The course is team-taught by theoretical and empirical biologists.
- Each student gets a hi-spec linux laptop for the course duration.
- You will be immersed in a collaborative learning environment, consistent with the interdisciplinary nature of modern biology.
- The course is focused on current topics in modern computational and quantitative biology such as the interactions between ecological and evolutionary dynamics, the effects of climate change on biological systems, and complex ecological and evolutionary networks.
- Mathematical, statistical, and computing tools are learned through application.
- The course comprises a taught component including core computing and statistical modules, and a research project lasting about 5.5 (MSc) or 9 (MRes) months.
- The research project can be supervised by multi-disciplinary Imperial College faculty (across departments and campuses), or through links to a number of outside institutes including Kew Botanical Gardens, the Natural History Museum, Centre for the Environment, Fisheries and Aquaculture Science, and the Institute of Zoology.
Modules in the taught component of the course include:
- Foundational topics in computing with the powerful combination of Python & R
- Scientific data management in the Unix environment
- Numerical computing in biology
- Advanced and parallel computing in biology
- Data structures, memory managment, and C
- Spatial analyses and Geographical Information Systems (GIS)
- Eco-evolutionary dynamics and demography
- Population genetics and genomics
- Modelling complex communities and ecological networks
- Inference of biological mechanisms from temporal or spatial data
- Microbial diversity, molecular ecology
- Maximum likelihood and Bayesian methods
- Generalized linear modelling in biology
'As an overseas student in Clinical Medicine, I chose CMEE because I thought computational methods in E &E can be applied to many research fields, together with my love for nature. It turned out to be more than I expected. The course modules were carefully designed so that it was not that scary as it seemed to be for a new programmer. The project module was also flexible, and I chose to study the population genetics of one pathogenic fungus, and look into the dynamic changes in their drug resistance genes. I will continue to apply what I have learned from CMEE to medicine, which I enjoyed so much during my rich and wonderful year! ' - Jianing Zhu, Class of 2016-17. Jianing resumed her Joint Masters program in Clinical Medicine at Zhejiang University in China in October 2017.
'The CMEE course is awesome. A mixture of crying (at the beginning) and then so much joy when you finally get an exercise done. I can tell that if you are not familiar with programming (like me), the course is very challenging but at the end, you will not regret to take the course as you will be prepared to work in computing either for industry as well as science'. Julian Perez-Correa, class of 2016-17. Julian founded the company PRIDA S.A., an Ecuadorian consultancy group for science and research.'By teaching a distinct skill-set, the course is invaluable in helping students become attractive candidates for potential PhD advisors and employees. Moreover the computational,mathematical and statistical skills learnt in CMEE form the bed-rock of most modern life sciences research. I cannot recommend this course highly enough, regardless of whether students are interested in computational methods in their own right, or want to learn these skills in order to complement other aspects of there research.' - Jean Vila, class of 2015-2016. Jean started a PhD student in the laboratory of Alvaro Sanchez at Yale University on eco-evolutionary feedbacks in microbial communities.
'Choosing the CMEE MSc was one of the best decisions I have ever made. This course allowed me to transition from a purely ecological background into the world of biological computing which has opened numerous doors for me- including helping me secure a PhD. CMEE sits at the intersection of biology, computer science, and math. As biologists generate ever larger datasets, the demand for people with this skillset who are able to not only analyse this data computationally but also interpret in its biological context will only increase. I believe that everyone who applied for PhDs in my cohort was offered at least one position before the course had even ended! As a CMEE student, it was obvious to me that the course organisers and lectures had put lots of effort into the design and execution of the course. It was this level of commitment that makes CMEE stand out from other masters degrees in my opinion. They provided multiple extra help sessions, gave regular feedback on assignments, fostered a collaborative environment, and gave invaluable advice on next career steps. I can’t recommend CMEE enough, whatever your background!' - Leanne Massie, Class of 2015-16. Leanne started a PhD in Bioinformatics and Theoretical Systems Biology in Michael Stumpf's group at Imperial College London in 2016.
'The CMEE course is an excellent choice for anyone looking for a career, in academia or industry, as a quantitative biologist. The Msc course has given me a lot of computational experience and a good idea of what PhD research will entail. I also think Silwood park has one of the most friendly and close-knit academic communities.' - Matishalin Patel, Class of 2014-15. Mat started a PhD at Oxford in the Interdisciplinary Bioscience DTP.
'The MSc in CMEE allowed me to combine two of my key interests, biology and maths, while also allowing me to develop new skills such as computer programming and modelling. I could then put these skills into practice during my research project in Iceland, from data collection right through to analysis, which was hugely satisfying. The CMEE course puts you in a fantastic place for beginning a research career' - Louise Archer, Class of 2014-15. Louise started a PhD with Tom Reed at University College Cork, Ireland on evolution and ecology of alternative life histories in brown trout.
'Biologists who possess mathematical and computational skills are becoming increasingly popular with potential supervisors and employers; Quantitative Biology is therefore the perfect stepping stone.' - Rebecca Spriggs, class of 2011, became a PhD student in the laboratory of David Coomes, Department of Plant Sciences, Cambridge University.
'This course helped me bring several interests together in preparation for a PhD. A fantastic opportunity for all biology students looking to develop.” – Sean Tuck, class of 2012. Sean was accepted to become a PhD student in laboratory of Andy Hector, Oxford University.
The Cultural Evolution of Evolution. 2017. Marina Papadopoulou. Marina started a PhD in computational models of collective behaviour of bird flocks at the University of Groningen in October 2017.
An exploration of linkage disequilibrium measurement and interpretation of decay using next generation sequencing data. 2017. Emma Fox. Emma started a PhD in Ecology and Evolutionary Biology at UCLA in September 2017.
Is modelling the physiological niche of Pacific bluefin tuna (Thunnus orientalis) a better method of predicting movement than the ecological niche? 2017. Rebekka Allgayer. Rebekka started a PhD on Integrating fish tracking and genetics to inform conservation management of Atlantic salmon at The University of Aberdeen in October 2017.
A Mechanistic Model of Bacterial Evolutionary Responses to Antibiotics in Communities of Varying Size. 2016. Leanne Massie. Leanne started a PhD in Bioinformatics and Theoretical Systems Biology in Michael Stumpf's group at Imperial College London in 2016.
The effects of environmental temperature and thermal adaptation on predator-prey interactions. 2015, Louise Archer. Louise started a PhD with Tom Reed at University College Cork, Ireland on evolution and ecology of alternative life histories in brown trout.
Individual-Based Modelling of Microbial Community Invasions. 2016, Jean Vila. Jean started a PhD student in the laboratory of Alvaro Sanchez at Yale University on eco-evolutionary feedbacks in microbial communities.
Modelling the evolution of gene-specific ecological divergence in bacteria. 2016, Michael Schmutzer. Michael started a PhD with Andreas Wagner at the University of Zurich.
Assessing general models for the temperature dependence of population density in disease vectors. 2015, Tom Smallwood. Tom started a PhD on the ecology of infectious diseases with Rosie Woodroffe at ZSL.
Why Do Whales Exist?: An Investigation into Cancer Resistance in Cetaceans. 2015, Matthew Speight.Matthew Started a PhD in Interdisciplinary Biosciences at Oxford University.
Linking the Impact of Selective Logging with Seedling Recruitment Dyanmics. 2015, Michael Massam. Michael started a PhD in the Edwards lab at Sheffield University on optimising logging strategies in the Brazilian Amazon to minimise biodiversity loss.
Modelling the relationship between local biodiversity and remotely-sensed vegetation indices: the effect of spatio-temporal scale. 2012, Sean Tuck. Sean was accepted to become a PhD student in the laboratory of Andy Hector, Oxford University. Also see MODISTools: an R package for retrieval and processing of remote-sensing data from NASAs MODIS satellites.
Finding value in SAD moments: a novel approach to upscaling species-abundance distributions. 2012. Hercules Araclides. Hercules was accepted to become a PhD student, beginning September 2012, in the laboratories of Pierre Legendre, University of Montreal, and Jonathan Davies, McGill University, Canada.
Eco-evolutionary dynamics in the bighorn sheep: linking population growth, trait variation and heritability of body mass. 2012. Michela Busana. Michela became a PhD student in a joint studentship between the University of Sheffield and the University of Groningen
Investigating climate change extinction risks of amphibians by simulation: the importance of life history and thermal performance traits. 2012. Bonnie Mappin. Bonnie became a Research Assistant in the laboratory of Simon Hay, Spatial Ecology and Epidemiology Group, Department of Zoology, Oxford University. She is working on the Malaria Atlas Project.
Testing for a random walk hypothesis with or without measurement error. 2012. Tin-Yu Hui. Tin-Yu became a PhD student in the laboratory of Austin Burt, Division of Ecology and Evolution, Imperial College London.
Modelling the impact of a keystone species on community diversity and stability. 2012. Jon Hamley. Jon became a PhD student in the laboratory of Jacob Koella, University of Neuchatel.
Survival of an exploited grey wolf population in the Northern Rocky Mountains: density dependence and licensed hunting. 2011. Jack Massey. Jack became a PhD student in the laboratory of Tim Coulson, Oxford University.
Demographic and evolutionary implications of lion body size: the application of an integral projection model to a large carnivore. 2011. Rebecca Spriggs. Rebecca became a PhD student in the laboratory of David Coomes, Department of Plant Sciences, Cambridge University.
Predicting induced chaos in the population dynamics of an insect species. 2011. Dimitrios Nerantzis. Dimitrios became a PhD student in the laboratory of Claire Adjiman, Department of Chemical Engineering, Imperial College London.
A synergy between wild and commercial: bio-economic modelling of python farming. 2011. Carolina Feijao. Carolina became a PhD student in the laboratory of Paul Dupree, Department of Biochemistry, Cambridge University.
- Dr. Samraat Pawar (course director)
- Dr. James Rosindell (course co-director)
- Prof. Timothy Barraclough
- Dr. Tom Bell
- Prof. Vincent Jansen
- Prof. Koenraad Audenaert
- Prof. Austin Burt
- Dr. Jason Hodgson
- Dr. Brian Hollis
- Dr. Rob Ewers
In addition, guest lectures and modules on specialized topics are delivered by members from the Maths, Engineering and Computer Science Departments at Imperial College, including the Centre for Complexity Science.
How to apply
All applications to the programme must be made online through the Imperial College applicatin site. You should read the application instructions carefully before submitting your application. For additional information on how to apply please contact the Postgraduate Administrator; telephone +44 (0)20 7594 2251; email email@example.com.
Links with Employers
Imperial College works closely with employers and industry, including Industrial Advisory Panels to design Master’s courses which provide graduates with technical knowledge, expertise and transferable skills and to encourage students to take internships and placements. All Master’s courses are designed with employer needs in mind with some Master’s courses accredited by Professional, Statutory and Regulatory Bodies. Most Master’s courses offer an opportunity to carry out research projects in industry.
Entry Requirements & Fees
The course is open to students with a broad range of biological degrees including biology, zoology, plant sciences, microbiology and environmental sciences, as well as bioinformatics, engineering, mathematics, computer science, or physical sciences. The minimum expected requirement is an Upper Second Class Honours degree in a relevant subject from a UK academic institution, or an equivalent overseas qualification.
In addition, for CMEE MSc, a minimum of B at UK A-level maths AND/OR a degree AND/OR work/research experience that demonstrates sufficient knowledge of maths is required. Candidates with non-biological backgrounds (for example maths, physics, or geography) or qualifications below the minimum level of the College academic regulations i.e. a lower Second Class Honours degree will need to convince the course directors of their interest, eligibility and/or suitability through their statement of intent in their application, and if necessary, through an informal interview. For CMEE MRes, UK A-levels knowledge of Maths is not required, but is desirable. Candidates with non-biological backgrounds (for example maths, physics, or geography) or qualifications below the minimum level of the College academic regulations i.e. a lower Second Class Honours degree will need to convince the Course Directors of their interest, eligibility and/or suitability through their statement of intent in their application, and if necessary, through an informal interview.
Applicants must also meet the College's English language requirements which for students outside the English speaking world are: IELTS 6.5 (plus writing and speaking 6.0); TOEFL Internet 100 (plus writing and speaking 24).