The M.Sc. by Research in the Mathematics of Complex Systems is designed to cater to interdisciplinary research aspirants as well as to fast tracked professionals who wish to pursue high profile academic or professional careers based on their
numerical proficiency.
The programme is epistemologically routed through a condensed taught phase spanning three months, followed by a nine months’ research project. Research topics are chosen in reconnaissance with students and cover an interdisciplinary gamut spanning biology to finance and are based on active areas of research interest in the Non-linearity and Complexity Research Group (NCRG). Students groomed through this programme are expected to make seamless transition to a career in academic research (Ph.D.) as well as in associated industrial profiles.
Who is it for?
This programme is for aspiring research leaders and industrial career builders, including computer-based professionals who wish to excel. Our expertise is in
delivering top quality research scientists, computer professionals and financial enthusiasts who will serve their professions at the topmost echelons.
What shall we deliver and how?
This M.Sc. will career guide top bracket students through a combination of taught and research led programmes inspired on interdisciplinary research ideas. Each research project is appraised through a combination of a written thesis and an oral
examination, and are often delivered in conjunction with industries, thereby offering students multiple routes towards career progression. Successful projects are expected to prosper in to top flight refereed publications.
The programme offers the possibility to extend the M.Sc. into a Ph.D.
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Algorithms and Computational Mathematics AM40AC (10 credits)
The module prepares students with a principled approach to numerical computation essential for research. In particular:
techniques from linear algebra and multivariate calculus, fundamental principles of mathematical computation and
numerical analysis to analyse algorithms, algorithms for differentiation, interpolation, integration, optimisation and linear
algebra, and the implementation and application of algorithms to complex systems applications.
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Methods of Statistical Physics AM40MP (10 credits)
The module introduces students to important concepts and modern methods of Statistical Physics and their application
to problems in related areas. It provides an in-depth analysis of some selected topics from Statistical Physics. It also
provides the students with a clear understanding of the need for independent thinking and the difficulties that may arise
when selecting/applying these methods to concrete problems and enables them to carry out research in this area.
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Probabilistic Modelling AM40PM (15 credits)
The module introduces the main concepts of probabilistic models and Bayesian methods to postgraduate students,
enabling them to carry out research in this area as part of an MSc by Research Programme. It covers topics such as
classification, Bayesian inference and decision theory, together with Gaussian mixture models, graphical models,
inference in belief networks, Markov processes, the Bayesian approach, and Monte Carlo methods.
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Stochastic and Non-Linear Dynamical Systems AM40SD (15 credits)
This module gives a concise introduction to modern methods and concepts of dynamical systems. This will expound
two views, based on deterministic partial differential equations and stochastic differential equations. The emphasis of
the course is on the application of mathematical methods rather than on mathematical rigour. Both analytical and
numerical methods will be considered with applications to physics, finance, earth sciences, biology etc. Lab based
computational works will be undertaken.
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Research Project (130 credits)
Each project is supervised by a member of academic staff, and supported by a second supervisor in an advisory role. In
cases of an industrial project there may be an additional industrial supervisor. The research project is assessed through a
research thesis and on an oral examination. The individual research project is a substantial piece of research in the
general field of complexity, the specifics of which will be worked out in collaboration with the supervisor. Note that as an
integral part of the research project, the students are required to follow a Research Skills module and complete individual
assignments (pass/fail) on specific topics. In addition students may be required by their project supervisor to attend some
short course(s) from the Research Skills Training Programme.
Applicants must produce evidence of being awarded a minimum of a 2:2 Honours degree in a numerate science from a UK University, or equivalent qualification. The Programme Director may conduct an (telephone) interview.
International students whose native language is not English will also need to demonstrate English Language ability (e.g. IELTS 6.5,
TOEFL score of 600 or equivalent).
This MSc requires the student to undertake 4 taught modules, together with the completion of a major project. The taught modules involve 22-33 teaching hours (depending on whether it is a 10 or 15 credit module), and may include tutorials, case studies and guest lectures. The programme runs from October to September with all taught modules being taught in term 1 (October to December). Upon successful completion of the taught component the project starts in January, and occupies nine months, being submitted in September.
Assessment methods are designed to meet the particular requirements of each module, but all taught modules are 100% continuous assessment. The research project is assessed by the research thesis and an oral presentation (viva) and counts for 130 of the 180 credits required for successful completion of the degree.
On successful completion of this programme, students will be able to understand and use a wide range of techniques from Complex Systems which they can interpret in a principled framework. They will understand the whole research process including non-technical issues, have developed independent learning and self-management skills, and transferable skills in numerical computing and oral and written communication. They will have carried out research in collaboration with an academic (and possibly industrial) partner, and have contributed to an active research programme.
The programme has been specifically designed to meet the huge demand for mathematically skilled experts in complexity science. Graduates may be employed in a broad range of positions: research and development teams as a part of a large company, in start-ups, or as consultants. We expect a substantial fraction of graduates from this programme to join research teams in high-tech engineering and IT sectors. Previous evidence suggests that such employment prospects are good. Projects in collaboration with an industrial partner have proven to be an excellent opportunity to forge links with industry that may result in subsequent employment, and students interested in such projects will be encouraged.
Dr Amit Chattopadhyay
Dr Amit Chattopadhyay is the Programme Director for the Masters Degree in the Mathematics of Complex Systems.
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