Our Computational Ecology MSc gives you knowledge and skills to work with ecological ‘big data’. It focuses on mathematical and statistical theory necessary for data interpretation and analysis.
You will be suitable for the course if you are:
WHAT YOU''LL LEARN:
Our course was created to close the knowledge gap in ecological research. It provides a sound grounding in important components of any research project, including:
We will teach you how to handle a range of data eg:
After successfully completing the course you will have the skills to undertake research projects in academia or industry.
A minimum of 30 credits is required for the degree, excluding bridge courses. The graduate curriculum consists of seven core courses and additional elective courses, with an optional thesis (six credits) or research project (three credits).
Select two of the following:
Ecology and evolutionary biology offer a perspective on biology from the level of genes to communities of species.
In the master's degree program, you can become familiar with a wide variety of topics in three areas: ecology, evolutionary biology and conservation biology. You can choose studies from any of these areas, as well as from other master's degree programmes. The programme is diverse and multidisciplinary: teaching is done with lectures, laboratory and computer training courses, interactive seminars, study tours and field courses. The field courses range from the northern subarctic region to tropical rainforests.
Our wide expertise extends from molecular ecology to population and community biology. The Centres of Excellence of Metapopulation Biology and Biological Interactions are located in our department.
Our programme offers you a wide range of options: evolutionary biology or genetics for those interested in ecological genetics and genomics, as well as the ability to take advantage of the high-quality molecular ecology and systematics laboratory; conservation biology for those interested in regional or global environmental problems; and ecological modelling skills for those interested in computational biology. Our training also offers Behavioural Ecology.
Ecology, evolutionary biology and conservation biology are not only fascinating topics for basic research, they also have a key role in addressing global environmental challenges.
Upon graduating from the Master's degree in ecology and evolutionary biology programme, you will:
Further information about the studies on the Master's programme website.
The Master's degree program includes studies of ecology, evolutionary biology and conservation biology. The studies are organised in modules. You can affect the content of the studies by planning your personal curriculum. You can study the following themes:
This MRes is designed for students with a passion for the diversity of life on earth, who are looking to further their knowledge and experience of quantitative techniques in ecology, evolution and conservation.
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.
You gain postgraduate-level training in research skills during the intensive one-year full-time programme.
You graduate with a range of transferrable professional skills that help to give a major edge in competing for PhDs and jobs. Your experience of conducting research projects will allow you to make a more informed decision on the area of research and specific PhD project you may wish to undertake in the future.
This course is the ideal training for students looking to pursue a career in academic, government or non-governmental organisations engaged in research into biodiversity.
For full information on this course, including how to apply, see: http://www.imperial.ac.uk/study/pg/life-sciences/computational-methods-ecology-evolution-mres/
If you have any enquiries you can contact our team at: [email protected]
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:
The course serves as ideal preparation for either PhD studies or employment in fields of applied quantitative biology, such as resource management and conservation.
For full information on this course, including how to apply, see: http://www.imperial.ac.uk/study/pg/life-sciences/computational-methods-ecology-evolution/
If you have any enquiries you can contact our team at: [email protected]
Life Sciences is one of the strategic research fields at the University of Helsinki. The multidisciplinary Master’s Programme in Life Science Informatics (LSI) integrates research excellence and research infrastructures in the Helsinki Institute of Life Sciences (HiLIFE).
The Master's Programme is offered by the Faculty of Science. Teaching is offered in co-operation with the Faculty of Medicine and the Faculty of Biological and Environmental Sciences. As a student, you will gain access to active research communities on three campuses: Kumpula, Viikki, and Meilahti. The unique combination of study opportunities tailored from the offering of the three campuses provides an attractive educational profile. The LSI programme is designed for students with a background in mathematics, computer science and statistics, as well as for students with these disciplines as a minor in their bachelor’s degree, with their major being, for example, ecology, evolutionary biology or genetics. As a graduate of the LSI programme you will:
Further information about the studies on the Master's programme website.
The Life Science Informatics Master’s Programme has six specialisation areas, each anchored in its own research group or groups.
Algorithmic bioinformatics with the Genome-scale algorithmics, Combinatorial Pattern Matching, and Practical Algorithms and Data Structures on Strings research groups. This specialisation area educates you to be an algorithm expert who can turn biological questions into appropriate challenges for computational data analysis. In addition to the tailored algorithm studies for analysing molecular biology measurement data, the curriculum includes general algorithm and machine learning studies offered by the Master's Programmes in Computer Science and Data Science.
Applied bioinformatics, jointly with The Institute of Biotechnology and genetics.Bioinformatics has become an integral part of biological research, where innovative computational approaches are often required to achieve high-impact findings in an increasingly data-dense environment. Studies in applied bioinformatics prepare you for a post as a bioinformatics expert in a genomics research lab, working with processing, analysing and interpreting Next-Generation Sequencing (NGS) data, and working with integrated analysis of genomic and other biological data, and population genetics.
Biomathematics with the Biomathematics research group, focusing on mathematical modelling and analysis of biological phenomena and processes. The research covers a wide spectrum of topics ranging from problems at the molecular level to the structure of populations. To tackle these problems, the research group uses a variety of modelling approaches, most importantly ordinary and partial differential equations, integral equations and stochastic processes. A successful analysis of the models requires the study of pure research in, for instance, the theory of infinite dimensional dynamical systems; such research is also carried out by the group.
Biostatistics and bioinformatics is offered jointly by the statistics curriculum, the Master´s Programme in Mathematics and Statistics and the research groups Statistical and Translational Genetics, Computational Genomics and Computational Systems Medicine in FIMM. Topics and themes include statistical, especially Bayesian methodologies for the life sciences, with research focusing on modelling and analysis of biological phenomena and processes. The research covers a wide spectrum of collaborative topics in various biomedical disciplines. In particular, research and teaching address questions of population genetics, phylogenetic inference, genome-wide association studies and epidemiology of complex diseases.
Eco-evolutionary Informatics with ecology and evolutionary biology, in which several researchers and teachers have a background in mathematics, statistics and computer science. Ecology studies the distribution and abundance of species, and their interactions with other species and the environment. Evolutionary biology studies processes supporting biodiversity on different levels from genes to populations and ecosystems. These sciences have a key role in responding to global environmental challenges. Mathematical and statistical modelling, computer science and bioinformatics have an important role in research and teaching.
Systems biology and medicine with the Genome-scale Biology Research Program in Biomedicum. The focus is to understand and find effective means to overcome drug resistance in cancers. The approach is to use systems biology, i.e., integration of large and complex molecular and clinical data (big data) from cancer patients with computational methods and wet lab experiments, to identify efficient patient-specific therapeutic targets. Particular interest is focused on developing and applying machine learning based methods that enable integration of various types of molecular data (DNA, RNA, proteomics, etc.) to clinical information.
Today more than ever, quantitative skills form an essential basis for successful careers in ecology, conservation, and animal and human health. This Masters programme provides specific training in data collection, modelling and statistical analyses as well as generic research skills. It is offered by the Institute of Biodiversity, Animal Health and Comparative Medicine (IBAHCM), a grouping of top researchers who focus on combining field data with computational and genetic approaches to solve applied problems in epidemiology and conservation.
The programme provides a strong grounding in scientific writing and communication, statistical analysis, and experimental design. It is designed for flexibility, to enable you to customise a portfolio of courses suited to your particular interests.
You can choose from a range of specialised options that encompass key skills in
A total of 180 credits are required, with 50 flexible credits in the second term. See the accompanying detailed course descriptions found in the IBAHCM Masters Programme Overview. When selecting options, please email the relevant course coordinator as well as registering using MyCampus.
You will gain core skills and knowledge across a wide range of subjects that will enhance your selection chances for competitive PhD programmes. In addition to academic options, career opportunities include roles in zoos, environmental consultancies, government agencies, ecotourism and conservation biology, and veterinary or public health epidemiology.
Our programme will give you cross-disciplinary skills in a rare combination of areas of expertise, from bioinformatics and evolutionary inference to computational biology and fieldwork.
You will be taught by researchers who apply genomic methods to a wide range of issues in ecology and evolution, from bat food-webs and genome evolution to microbial biodiversity in natural and engineered ecosystems. For example, Professor Steve Rossiter carries out world-leading research on bat genome evolution; Dr Yannick Wurm has discovered a social chromosome in fire-ants; and Dr China Hanson is using genetic methods to study microbial biogeography. This means that teaching on our programme is informed by the latest developments in this field, and your individual research project can be at the forefront of current scientific discovery.
You will conduct your own substantive six-month research project, which may be jointly supervised by contacts from related institutes or within industry. You will also take part in a field course in Borneo - see photos from a recent trip on Flickr - giving you the opportunity to develop first hand experience of theory in action.
By choosing to study at a Russell Group university you will have access to excellent teaching and top class research. You can find out more about our research interests and view recent publications on the School of Biological and Chemical Science's Evolution and Genetics group page.
This MSc programme combines taught modules with individual and collaborative research projects. You will apply the knowledge and techniques from your taught modules in a practical setting and may be able to publish your project findings.
If you have any questions about the content or structure, contact the programme director Dr Christophe Eizaguirre.
This MSc provides an ideal foundation for students wishing to advance their mathematical modelling skills. The programme teaches students the basic concepts which arise in a broad range of technical and scientific problems and illustrates how these may be applied in a research context to provide powerful solutions.
Students develop an understanding of the processes undertaken to arrive at a suitable mathematical model and are taught the fundamental analytical techniques and computational methods used to develop insight into system behaviour. The programme introduces a range of problems - industrial, biological and environmental - and associated conceptual models and solutions.
Students undertake modules to the value of 180 credits.
The programme consists of five core modules (75 credits), three optional modules (45 credits), and a research dissertation (60 credits).
The part-time option normally spans two years. The eight taught modules are spread over the two years. The research dissertation is taken in the summer of the second year.
All MSc students undertake an independent research project, which culminates in a dissertation of approximately 15,000-words and a project presentation.
Teaching and learning
The programme is delivered through seminar-style lectures and problem and computer-based classes. Student performance is assessed through a combination of unseen examination and coursework. For the majority of courses, the examination makes up between 90–100% of the assessment. The project is assessed through the dissertation and an oral presentation.
Further information on modules and degree structure is available on the department website: Mathematical Modelling MSc
Our graduates have found employment in a wide variety of organisations such as Hillier-Parker, IBM, Swissbank, Commerzbank Global Equities, British Gas, Harrow Public School, Building Research Establishment and the European Centre for Medium-Range Weather-Forecasting.
Recent career destinations for this degree
Finance, actuarial and accountancy professionals are constantly in demand for their high-level mathematical skills and recent graduates have taken positions in leading finance-related companies such as UBS, Royal Bank of Scotland, Societe Generale, PricewaterhouseCoopers, Deloitte, and KPMG.
In the engineering sector, one recent graduate has progressed to a mathematical modelling role at a leading transportation planning consultancy; another became a graduate trainee at a business segment of Schlumberger that provides reservoir imaging, monitoring, and development services.
In addition, a number of graduates have remained in education either progressing to a PhD or entering the teaching profession.
Careers data is taken from the ‘Destinations of Leavers from Higher Education’ survey undertaken by HESA looking at the destinations of UK and EU students in the 2013–2015 graduating cohorts six months after graduation.
UCL Mathematics is internationally renowned for its excellent individual and group research that involves applying modelling techniques to problems in industrial, biological and environmental areas.
The department hosts a stream of distinguished international visitors. In recent years four staff members have been elected fellows of the Royal Society, and the department publishes the highly regarded research journal Mathematika.
This MSc enables students to consolidate their mathematical knowledge and formulate basic concepts of modelling before moving on to case studies in which models have been developed for issues motivated by industrial, biological or environmental considerations.
The Research Excellence Framework, or REF, is the system for assessing the quality of research in UK higher education institutions. The 2014 REF was carried out by the UK's higher education funding bodies, and the results used to allocate research funding from 2015/16.
The following REF score was awarded to the department: Mathematics
82% rated 4* (‘world-leading’) or 3* (‘internationally excellent’)
Learn more about the scope of UCL's research, and browse case studies, on our Research Impact website.
This programme will teach you how to manage and manipulate large datasets to reveal new insights in biological sciences. You will get intensive training in a computer-based approach to biological research, with the opportunity to develop specialist skills in computer programming, data analysis, statistics and computational biology.
New analytical techniques deliver ever more data about genes, proteins, metabolites and the interactions between them. Bioinformatics is the discipline tasked with turning all this data into useful information and new biological knowledge. With applications spanning the breadth of life science disciplines, there is now high demand for trained bioinformaticians.
Prior experience of computer programming is not required as you will be taught the latest tools and techniques in bioinformatics, which you will then apply to your own research project. You will also collaborate with peers to build new bioinformatics solutions to real-world problems as part of an innovative group project.
This programme is delivered by academics who are actively engaged in developing bioinformatics tools and applying them in areas such as genome sequencing, proteomics, evolution, ecology, psychology, cancer, diabetes and other diseases. We have an extensive network of academic and industrial collaborators around the UK and in Europe, who contribute to teaching, co-supervise projects and provide employment opportunities.
By choosing to study at a Russell Group university you will have access to excellent teaching and leading research. Our staff draw heavily upon their industrial or research council-funded research to inform their teaching and ensure projects are topical and well-resourced.
Developing bioinformatics has allowed me to work in one of the best companies when it comes to smart science. They help me understand our customers’ needs as they seek to implement bioinformatics pipelines to extract new knowledge from the data generated by our sequencers
Yasmine (Bioinformatics 2016 graduate, currently working at Illumina)
If you have questions about this programme which you would like to put to Professor Conrad Bessant, Professor of Bioinformatics and Bioinformatics MSc Programme Director, please contact:
Tel: +44(0)207 882 6510
Email: [email protected]