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Masters Degrees (Mathematical Modelling)

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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. Read more

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.

About this degree

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.

Core modules

  • Advanced Modelling Mathematical Techniques
  • Nonlinear Systems
  • Operational Research
  • Computational and Simulation Methods
  • Frontiers in Mathematical Modelling and its Applications

Optional modules

  • Asymptotic Methods & Boundary Layer Theory
  • Biomathematics
  • Cosmology
  • Evolutionary Game Theory and Population Genetics
  • Geophysical Fluid Dynamics
  • Mathematical Ecology
  • Quantitative and Computational Finance
  • Theory of Traffic Flow
  • Waves and Wave Scattering

Dissertation/report

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

Careers

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

  • Actuarial Analyst, KPMG
  • Data Scientist, Echobox
  • Graduate Technical Professional, AVEVA
  • PhD in Biochemical Engineering, UCL
  • Engineer, Erds (EDF)

Employability

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.

Why study this degree at UCL?

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.

Research Excellence Framework (REF)

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.



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This programme develops mathematical modelling skills and provides mathematical techniques required by industry. The period October to June is devoted to lectures, tutorials and practical sessions comprising the core modules. Read more
This programme develops mathematical modelling skills and provides mathematical techniques required by industry.

The period October to June is devoted to lectures, tutorials and practical sessions comprising the core modules.

This is followed by a period of about 14 weeks devoted to an individual project either in an industrial or engineering company or at the University.

Core study areas include mathematical modelling, regular and chaotic dynamics, programming and numerical methods, advanced reliability, availability and maintainability, elements of partial differential equations, static and dynamic optimisation and fluid mechanics.

See the website http://www.lboro.ac.uk/study/postgraduate/programmes/departments/mathematics/industrial-maths-modelling/

Programme modules

Compulsory Modules:
Semester 1
- Mathematical Modelling I
- Regular and Chaotic Dynamics
- Programming and Numerical Methods
- Advanced Reliability, Availability and Maintainability

Semester 2
- Mathematical Modelling II
- Elements of Partial Differential Equations
- Static and Dynamic Optimisation
- Fluid Mechanics

Assessment

A combination of written examinations, reports, individual and group projects, and verbal presentations.

Careers and further Study

Graduate employment over a wide range of industries encompassing aerospace, automotive electronics, and computer interests as well as software houses, insurance companies, and research establishments and institutions.

Scholarships and sponsorships

A limited number of scholarships are available for this programme as well as the loyalty bonus scheme which reduces fees for Loughborough graduates.

Why choose mathematics at Loughborough?

Mathematics at Loughborough has a long history of innovation in teaching, and we have a firm research base with strengths in both pure and applied mathematics as well as mathematics education.

The Department comprises more than 34 academic staff, whose work is complemented and underpinned by senior visiting academics, research associates and a large support team.

The programmes on offer reflect our acknowledged strengths in pure and applied research in mathematics, and in some cases represent established collaborative training ventures with industrial partners.

- Mathematics Education Centre (MEC)
The Mathematics Education Centre (MEC) at Loughborough University is an internationally renowned centre of research, teaching, learning and support. It is a key player in many high-profile national initiatives.
With a growing number of academic staff and research students, the MEC provides a vibrant, supportive community with a wealth of experience upon which to draw.
We encourage inquiries from students who are interested in engaging in research into aspects of learning and teaching mathematics at Masters, PhD and Post Doc levels. Career prospects With 100% of our graduates in employment and/or further study six months after graduating, career prospects are excellent. Graduates go on to work with companies such as BAE Systems, Citigroup, Experian, GE Aviation, Mercedes Benz, Nuclear Labs USA and PwC.

- Career prospects
With 100% of our graduates in employment and/or further study six months after graduating, career prospects are excellent. Graduates
go on to work with companies such as BAE Systems, Citigroup, Experian, GE Aviation, Mercedes Benz, Nuclear Labs USA and PwC.

Find out how to apply here http://www.lboro.ac.uk/study/postgraduate/programmes/departments/mathematics/industrial-maths-modelling/

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Postgraduate degree programme in Mathematical Modelling Masters/MSc. Most things in the real world are complex and difficult to understand, from biological systems to the financial markets to industrial processes, but explaining them is essential to making progress in the modern world. Read more

Postgraduate degree programme in Mathematical Modelling Masters/MSc:

Most things in the real world are complex and difficult to understand, from biological systems to the financial markets to industrial processes, but explaining them is essential to making progress in the modern world. Mathematical modelling is a fundamental tool in the challenge to understand many of these systems, and is an essential part of contemporary applied mathematics. By developing, analysing and interpreting mathematical and computational models we gain insight into these complex processes, as well as giving a framework in which to interpret experimental data. 

To fully capitalise on these tools, there is a fundamental need in both academic research and industry for a new generation of scientists trained to work at the interdisciplinary frontiers of mathematics and computation. These scientists require the ability to assimilate and understand information from other disciplines, communicate with and enthuse other researchers, as well as having the advanced mathematical and computational skills needed.

Course details

In the Autumn and Spring semesters, you will take masters-level courses in both advanced mathematical modelling and computation, in addition to the core interdisciplinary skills needed for a career in this field. In the summer you will undertake a research skills project, working with research leaders in a related area such as biosciences, systems biology, chemical engineering or medicine, alongside mathematics and computation. This will provide directly relevant training for a career in academic, industrial or clinical research, for example biotechnology, industrial engineering or the pharmaceutical industry. A key component will be training specifically in multidisciplinary research and communication, a vital skill for whichever career path the MSc leads you to. 

Related Links

Learning and teaching

In the Autumn and Spring semesters, you will take masters-level courses in both advanced mathematical modelling and computation, in addition to the core interdisciplinary skills needed for a career in this field. 

In the summer you will undertake a research skills project, working with research leaders in a related area such as biosciences, systems biology, chemical engineering or medicine, alongside mathematics and computation. This will provide directly relevant training for a career in academic, industrial or clinical research, for example biotechnology, industrial engineering or the pharmaceutical industry. 

A key component will be training specifically in multidisciplinary research and communication, a vital skill for whichever career path the MSc leads you to.

Employability

Career Opportunities

This course is tailored to train students for careers in scientific research, and for employment in a wide range of industrial contexts, for example biotechnology, industrial engineering or the pharmaceutical industry. There is a considerable need for scientists with a strong mathematical and computational background who can communicate with experimental scientists; this MSc will provide you with specialised training, and through your research skills project, evidence that you can work in this multidisciplinary context.

Further transferrable skills developed through this course include team-working, oral and written presentation, problem-solving and time-management, particularly developed through the summer research skills project. Additional careers support is available through the School of Mathematics and from the University's career support team.  



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About MathMods. MathMods is a. 2-year. Joint. MSc programme which can be taken in. 5 EU universities. Read more

About MathMods

MathMods is a 2-year Joint MSc programme which can be taken in 5 EU universities: University of L’Aquila in Italy (UAQ), Vienna University of Technology in Austria (TUW), Autonomous University of Barcelona in Spain (UAB), Hamburg University of Technology (TUHH) & University of Hamburg in Germany (UHH), and University of Nice - Sophia Antipolis in France (UNS).

What makes MathMods so special is its peculiar mobility scheme, that is the fact that our students will be spending their postgrad years in two or even three different European countries. You'll be indeed studying in central Italy for your first semester, then move to Austria or Germany for the second term, and finally move again to 1 of our 5 partners for your second year, based on the mobility path you'll be assigned. 

Upon graduation students will be awarded a Joint Master's degree (or double, depending on where they spend their Year2).

Since its establishement in 2008 MathMods was funded by the EU Commission firstly through the Erasmus Mundus programme action 1 A (project no. 2008-0100), and later through the Erasmus+ Key Action 1 programme, project no. 2013-0227. We're currently applying for the Erasmus+ Call for Proposals 2018 to continue awarding Erasmus Munuds scholarships to our future generations. No matter the outcome, MathMods will still be running with the aid of Consortium grants and other local grants. Visit the sections Apply and Program Structure to learn more.

Programme Structure

Semester 1 focuses on Theory and is to be spend in L'Aquila (Italy)

Semester 2 focuses on Numerics and can be taken in Vienna (Austria) or Hamburg (Germany).

Then for Year2 each partner institution offers a specific curriculum or study path:

  • Mathematical models in social sciences (UAQ, Italy)
  • Mathematical modelling and optimisation (UAQ, Italy)
  • Stochastic modelling and optimization (UAB, Spain)
  • Modelling and simulation of complex systems (TUHH or UHH, Germany)
  • Mathematical modelling applications to finance (UNS, France)
  • Advanced modelling and numerics for applied PDEs (TUW, Austria)

Semester4 is dedicated to thesis work.

Aims

Mathematical modelling refers to the use of mathematics and related computational tools to bring real-world, challenging and important socio-economic and industrial problems into a form simple enough so that a good solution can be found in a reasonable time, while keeping the relevant features of the problem. Constructing models requires knowledge of enough mathematical theory, methods of solution which are really effective and efficient, computational tools at hand to do it, some knowledge of the field of application, and communicative skills to understand the important elements from experts in that field. Our master's programme tries to put together all these elements to produce professionals able to work in different relevant fields with the highest intellectual level and state-of-the-art tools.

Effective modelling and simulation is an art that require a lot of practice, so that problem solving, project development and team work are aspects that should be highlighted in any training programme, as our Consortium knows perfectly. On the other hand, the abstraction behind the specific application is necessary to realise that the same base tools can be applied, with the needed changes, to very different situations in various engineering fields.

Language

The language of the whole course is exclusively English at each of our five universities. Students must also attend (and acquire the relating credits of) a course of basic Italian language (first semester) and German language (second semester). Students will also have the opportunity to attend local language courses during their second year (spent at one of the five partners).

Employability

The area of applied mathematics on which this project is focused is a fundamental scientific field for a number of key technologies and sciences. The areas of the proposed tracks connect very well with various branches of the European high-tech industry, and one of the goals of the project is to enhance these connections by means of the release of well prepared professionals and researchers.

Career opportunities for graduates will typically arise in research and development laboratories, especially those defining and testing numerical models and procedures, either working for an specific sector or with a broader scope. Also, in big or medium size enterprises possessing their own research department or a division with an orientation towards research, in public or privately held Sector Technology Centres, and at computing centres involved in data processing or the creation of numerical codes for the industry.

 Click here to view the results of a recent survey taken by our graduates about their overall experience in our MSc and their work experience after having graduated.

Academic opportunities

The graduates will be able to apply successfully for PhD programmes if they wish so, as has happened for the three already completed cycles. In all the countries of the Consortium members, the programme has been validated as enabling the holder of the MSc degree to enter a local PhD programme. A good percentage of our students seem indeed to prefer pursuing a PhD before going to the industry or returning to their countries of origin. The intended level of the programme, together with the initial selection of the students, allows affirming that most of them could follow a successful academic career, after a suitable PhD. 



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Self-learning systems are an important and newly emerging technique in many areas of applied science such as Applied Mathematics, Engineering, Computer Science and Statistics. Read more

Self-learning systems are an important and newly emerging technique in many areas of applied science such as Applied Mathematics, Engineering, Computer Science and Statistics. In particular, self-learning systems are a disruptive approach to mathematical modelling which use differential equations at their foundation. A particular strength of this approach is that it combines numerical learning algorithms such as dynamic machine learning with differential equations to design applications that can adapt to a changing environment. This approach is new and unique because it explicitly takes into account the dynamic aspects of data and allows for fast and accurate modelling of self-learning systems. This is a new and rapidly developing area at the interface between applied mathematics and machine-learning (for example see here).

The primary aim of this course is to provide training in the use and development of modern numerical methods and self-learning software. Graduates will develop and apply new skills to real-world problems using mathematical ideas and techniques together with software tailored for complex networks and self-learning systems. While there is a strong focus on modern applications, graduates will gain in-demand skills in mathematical modelling, problem-solving, scientific computing, dynamic machine learning, complex networks and communication of mathematical ideas to a non-technical audience.

More general hands-on skills include mathematical typesetting, mathematical writing, desktop and web-based mathematical software development, and the use of computer languages and packages such as C#, R, Python and TensorFlow.

Course Practicalities

The course places great emphasis on hands-on practical skills. There is a computer laboratory allocated solely for the use of MSc students. PCs are preloaded with all the required software and tools. Teaching hours, tutorials and practical demonstrations, usually take place in the morning. The rest of the time, you are expected to do exercises, assignments and generally put in the time required to acquire key skills. For online modules, students are advised to have access to a laptop/home computer with internet connection, modern browser, word processing and spreadsheet software.

Why choose this course?

This MSc reflects a philosophy of cutting edge teaching methods and pragmatism. As well as providing you with a host of abilities which are in demand in industry, this MSc provides skills which are complementary to most scientific and engineering undergraduate courses. The MSc not only opens up new possibilities, you also gain a set of skills that sets you apart from the crowd in your original field of study. The final project is an excellent opportunity for you to showcase your abilities to future employers or to undertake a detailed study in a new area of interest. The course is extremely flexible in helping you realise your ambitions. 

Skills and Careers Information

Graduates with quantitative skills and expertise in self-learning algorithms are in high demand in industry according to the Governments Expert Group on Future Skills Needs. Demand for these skills is projected to rise over the coming years not just in Ireland but in the EU and globally. Graduates from a similar MSc have secured jobs in the following areas: banking, financial trading, consultancy, online gambling firms, software development, logistics, data analysis and with companies such as AIB, McAfee, Fexco, DeCare Systems, MpStor, the Tyndall Institute, Matchbook.com, First Derivatives and KPMG.




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This one-year master's course provides training in the application of mathematics to a wide range of problems in science and technology. Read more

This one-year master's course provides training in the application of mathematics to a wide range of problems in science and technology. Emphasis is placed on the formulation of problems, on the analytical and numerical techniques for a solution and the computation of useful results.

By the end of the course students should be able to formulate a well posed problem in mathematical terms from a possibly sketchy verbal description, carry out appropriate mathematical analysis, select or develop an appropriate numerical method, write a computer program which gives sensible answers to the problem, and present and interpret these results for a possible client. Particular emphasis is placed on the need for all these parts in the problem solving process, and on the fact that they frequently interact and cannot be carried out sequentially.

The course consists of both taught courses and a dissertation. To complete the course you must complete 13 units.

There are four core courses which you must complete (one unit each), which each usually consist of 24 lectures, classes and an examination. There is one course on mathematical methods and one on numerical analysis in both Michaelmas term and Hilary term. Each course is assessed by written examination in Week 0 of the following term.

Additionally, you must choose at least least one special topic in the area of modelling and one in computation (one unit each). There are around twenty special topics to choose from, spread over all three academic terms, each usually consisting for 12 to 16 lectures and a mini project, which culminates in a written report of around 20 pages. Topics covered include mathematical biology, fluid mechanics, perturbation methods, numerical solution of differential equations and scientific programming. 

You must also undertake at least one case study in modelling and one in scientific computing (one unit each), normally consisting of four weeks of group work, an oral presentation and a report delivered in Hilary term.

There is also a dissertation (four units) of around 50 pages, which does not necessarily need to represent original ideas. Since there is another MSc focussed on mathematical finance specifically, the MSc in Mathematical and Computational Finance, you are not permitted to undertake a dissertation in this field.

You will normally accumulate four units in core courses, three units in special topics, two units in case studies and four units in the dissertation. In addition, you will usually attend classes in mathematical modelling, practical numerical analysis and additional skills during Michaelmas term.

In the first term, students should expect their weekly schedule to consist of around seven hours of core course lectures and seven hours of modelling, practical numerical analysis and additional skills classes, then a further two hours of lectures for each special topic course followed. In addition there are about three hours of problem solving classes to go through core course exercises and students should expect to spend time working through the exercises then submitting them for marking prior to the class. There are slightly fewer contact hours in the second term, but students will spend more time working in groups on the case studies.

In the third term there are some special topic courses, including one week intensive computing courses, but the expectation is that students will spend most of the third term and long vacation working on their dissertations. During this time, students should expect to work hours that are equivalent to full-time working hours, although extra hours may occasionally be needed. Students are expected to write special topic and case study reports during the Christmas and Easter vacations, as well as revising for the core course written examinations.



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Visit the Institute for Transport Studies at our Masters. Open Days. Mathematical models are fundamental to how we understand, analyse and design transportation systems, but these models face challenges from the rapidly changing nature of mobility. Read more

Visit the Institute for Transport Studies at our Masters Open Days.

Mathematical models are fundamental to how we understand, analyse and design transportation systems, but these models face challenges from the rapidly changing nature of mobility.

Innovative technologies are being harnessed to deliver new approaches to transport services, and huge volumes of data create new opportunities to examine how patterns of movement are evolving.

If you’re a highly numerate graduate with a desire to apply your quantitative skills to the real world, or a practitioner working in the sector, this course will take you to the next level and prepare you for a career as a transport modelling specialist.

97% of our graduates find employment in a professional or managerial role, or continue with further studies.*

Experience a course designed in collaboration with employers, learning skills the industry desperately needs to unlock the full potential of big data.

Learn to think creatively, beyond the standard application of established solutions, and use your technical expertise across multiple scenarios.

Develop and apply mathematical models to analyse and improve the performance of transportation networks and flows:

  • Use mathematical models to represent transport systems and forecast demand
  • Test solutions and strategies using different models
  • Apply optimisation algorithms to traffic networks
  • Develop computer code to enhance and visualise outputs
  • Critically evaluate and adapt existing modelling techniques
  • Write scientific reports for technical and lay audiences
  • Develop research and advanced scholarship skills.

Experience what it is like to be part of a project team working across disciplinary boundaries within the transport sector. Through this, gain insights into how modelling, environmental science, planning, economics and engineering can work together to develop sustainable solutions to global challenges. This industry-inspired approach will enable you to apply your knowledge to real-world issues in the field.

Your colleagues will be among the best and brightest from the UK and across the globe. Together you will learn mathematical modelling skills that can be applied to design smarter transport solutions founded on robust methods.

With a strong focus on industry needs, our degrees will prepare you for employment in your chosen field. They will also address the multi-disciplinary nature of transport – enabling you to make effective decisions for clients, employers and society.

Other Study Options

This programme is available part time, allowing you to combine study with other commitments. You can work to fund your studies, or gain a new qualification without giving up an existing job. We aim to be flexible in helping you to put together a part-time course structure that meets your academic goals while recognising the constraints on your study time.

You can also study this subject at Postgraduate Diploma level, part time or full time, or at Postgraduate Certificate level with our PGCert in Transport Studies.




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Climate change is recognised as having potentially huge impacts on the environment and on human society. Read more
Climate change is recognised as having potentially huge impacts on the environment and on human society. This programme aims to provide an understanding of climate change causes, impacts, mitigation and adaptation measures from a life science perspective in conjunction with developing a wide variety of mathematical modelling skills that can be used to investigate the impacts of climate change.

The programme closely follows the structure of our Applied Mathematical Sciences MSc. Two of the mandatory courses will specifically focus on understanding the issues related to climate change and are taught by the School of Life Sciences.

Students will take a total of 8 courses, 4 in each of the 1st and 2nd Semesters followed by a 3-month Project in the summer. A typical distribution for this programme is as follows:

Core courses

Modelling and Tools;
Mathematical Ecology;
Climate Change: Causes and Impacts;
Climate Change: Mitigation and Adaptation Measures;
Dynamical Systems (recommended);
Stochastic Simulation (recommended)

Optional Courses

Optimization;
Mathematical Biology and Medicine;
Numerical Analysis of ODEs;
Applied Mathematics;
Statistical Methods;
Applied Linear Algebra;
Partial Differential Equations;
Numerical Analysis;
Geometry;
Bayesian Inference.

Typical project subjects

Population Cycles of Forest Insects;
Climate Change Impact;
The replacement of Red Squirrels by Grey Squirrels in the UK;
Vegetation Patterns in Semi-arid Environments;
Daisyworld: A Simple Land Surface Climate Model.

The final part of the MSc is an extended project in mathematical modelling the impacts of climate change on environmental systems, giving the opportunity to investigate a topic in some depth guided by leading research academics.

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What do Facebook, the financial system, Internet or the brain have in common?. "Everything is connected, all is network". Read more
What do Facebook, the financial system, Internet or the brain have in common?

"Everything is connected, all is network"
From the underlying skeleton of social relations, the interdependent evolution of our financial system, to the emergent collective computation in the brain, most of the complex systems that appear in society, technology, and nature are ultimately characterised by a nontrivial pattern of inter-relations. This underlying architecture is in turn shaping how information diffuses and spreads, how resilient the system is against attacks or perturbations, or how complex patterns emerge at the systemic level from the aggregation of seemingly simple individuals.

Our MSc Network Science will provide a thorough grounding in the core principles of modelling and analysis of complex and networked systems, along with the principal analytical and numerical methodologies. This will open to students a host of career opportunities in systems and networks modelling industries, spanning the IT, financial, and biomedical sectors, that are now requiring such specialist knowledge and skills.

Network Science is a very active and rapidly evolving research field with high societal impact, which stands at the crossroads of graph theory, complexity and data analysis. Addressing the description and modelling of the architecture and dynamics of complex systems -systems composed by many interacting units that show collective behaviour- it stands as a new kind of science to cope with some of the most challenging endeavours we face today, in an ever increasingly more connected society.
Its impact and applications outside academia pervades technological sectors such as communications and infrastructures (Internet, transportation networks, energy networks, urban mobility), finance (financial risk and systemic instability, financial networks, interbank cross-correlations), marketing and IT (social media, data analytics), public health (epidemic spreading models), or biostatistics and network biology (brain modelling, protein interaction networks, postgenomic era), to cite a few. This specialist masters programme aims at providing graduate students and professionals with a rigorous training in the underlying mathematical concepts, the analysis and modelling of complex networks and networked systems, complemented with training in computing, numerical simulations and massive data analysis. It is aimed towards students with a mathematical background who wish to enter a career involving analysis and optimisation of diverse kinds of networks, networked dynamics and models.

Why study your MSc Network Science at Queen Mary?
This is a pioneering MSc in the UK, a joint programme, taught by our Schools of Mathematical Sciences, and Electronic Engineering and Computer Science, drawing on their strengths in research and teaching in the area of complex networks, mathematical modelling of complex systems, and data mining.

We teach what we know and what we do best. Within the School of Mathematics, the Complex Systems & Networks group is one of the biggest hubs in Network Science within the UK, where we address both fundamental and applied challenges in the mathematical modelling of complex systems with clear societal impact, in collaboration with several industrial stakeholders. Within the School of Electronic Engineering, the Networks group was founded in 1987, and has hugely expanded ever since, bringing their expertise in online social networks, data mining and cloud computing. The coalescence of both groups expertises has fostered the creation of this unique MSc.

More about our two schools

Queen Mary is a member of the prestigious Russell Group of leading UK universities, combining world-class research, teaching excellence and unrivalled links with business and the public sector. The School of Mathematical Sciences has a distinguished history on itself. We have been conducting pioneering mathematical research since the 1950s, and as one of the largest mathematical departments in the UK, with over 50 members of staff, the school can offer diverse postgraduate study opportunities across the field, from pure and applied mathematics, to finance and statistics. Along with the MSc in Network Science, our cohort of postgraduate students specialise in Mathematics and Statistics, Mathematical Finance and Financial Computing. We are one of the UK’s leading universities in the most recent national assessment of research quality, we were placed ninth in the UK (REF 2014) amongst multi-faculty universities. This means that the teaching on our postgraduate programmes is directly inspired by the world-leading research of our academics. Our staff includes international leaders in many areas of mathematical research, and the School is a hive of activity, providing a vibrant intellectual space for postgraduate study.

The School of Electronic Engineering and Computer Science is internationally recognised for their pioneering and ground-breaking research in several areas including machine learning and applied network analysis. This expertise uniquely complements the more theoretical knowledge offered by the School of Mathematical Sciences, providing a well balanced mix of theory and applications and offering a deep and robust programme that combines the foundations of the mathematics of networks with the latest cutting edge applications in real world problems.

Additionally, Queen Mary holds a university-level Bronze Award for the Athena SWAN Charter, which recognises and celebrates good employment practice for women working in mathematics, science, engineering and technology in higher education and research.

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Computer Science is one of the drivers of technological progress in all economic and social spheres. Those graduating with an M.Sc. Read more

About Computer Science

Computer Science is one of the drivers of technological progress in all economic and social spheres. Those graduating with an M.Sc. in Computer Science are specialists in at least one field of computer science who have wide-ranging science-based methodological expertise.
Graduates are able to define, autonomously and comprehensively, computer science problems and their applications, structure them and build abstract models. Moreover, they are able to define and implement solutions that are at the state of the art of technology and science.

Features

– A broad, international and relevant selection of courses
– As a student, you will work on cutting-edge research projects
– Individual guidance in small learning groups
– Excellent enterprise relations maintained by the chairs and institutes
– Numerous partnerships with universities throughout the world, including a double degree programme with the Institut national des sciences appliquées de Lyon (INSA)

Syllabus

The programme offers the following five focus modules:
1) Algorithms and Mathematical Modelling
2) Programming and Software Systems
3) Information and Communication Systems
4) Intelligent Technical Systems
5) IT Security and Reliability
1) Algorithms and Mathematical Modelling: This module teaches you about determinstic and stochastic algorithms, their implementation, evaluation and optimisation. You will acquire advanced knowledge of computer-based mathematical methods – particularly in the areas of algorithmic algebra and computational stochastics – as well as developing an in-depth expertise in mathematical modelling and complexity analysis of discrete and continuous problems.
2) Programming and Software Systems: This module imparts modern methods for constructing large-scale software systems, as well as creating and using software authoring, analysis and optimisation tools. In this module you will consolidate your knowledge of the various programming paradigms and languages, the structure of language processing systems, and learn to deal with parallelism in program procedures.
3) Information and Communication Systems: In this module you will study the interactions of the classic computer science areas of information systems and computer networks. This focus area represents an answer to the problem of increasing volume and complexity of worldwide information distribution and networks, and for the growing requirements on quality and performance of computer communication. Additionally, you will learn to transfer database results to multimedia data.
4) Intelligent Technical Systems: In this module you are acquainted with digital image and signal processing, embedded systems and applications of intelligent technical systems in industrial and assistance systems, which are necessary for production automation and process control, traffic control, medical and building technology. You will learn to develop complex applications using computer systems and deal with topics such as image reconstruction, camera calibration, sensor data fusion and optical measurement technology.
5) IT Security and Reliability: This module group is concerned with security and reliability of IT systems, e.g. in hardware circuitry and communication protocols, as well as complex, networked application systems. To ensure the secure operation of these systems you will learn design methodology, secure architectures and technical implementation of the underlying components.

Language requirements

Unless English is your native language or the language of your secondary or undergraduate education, you should provide an English language certificate at level B2 CEFR, e.g. TOEFL with a minimum score of 567 PBT, 87 iBT or ITP 543 (silver); IELTS starting from 5.5; or an equivalent language certificate.

To facilitate daily life in Germany, it would be beneficial for you to have German language skills at level A1 CEFR (beginner’s level). If you do not have any German skills when starting out on the programme, you will complete a compulsory beginner’s German course during your first year of study.

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Visit our website for more information on fees, scholarships, postgraduate loans and other funding options to study Computer Modelling in Engineering at Swansea University - 'Welsh University of the Year 2017' (Times and Sunday Times Good University Guide 2017). Read more

Visit our website for more information on fees, scholarships, postgraduate loans and other funding options to study Computer Modelling in Engineering at Swansea University - 'Welsh University of the Year 2017' (Times and Sunday Times Good University Guide 2017).

This MRes in Computer Modelling in Engineering programme consists of two streams: students may choose to specialise in either structures or fluids. The taught modules provide a good grounding in computer modelling and in the finite element method, in particular.

Key Features of MRes in Computer Modelling in Engineering

Computer simulation is now an established discipline that has an important role to play in engineering, science and in newly emerging areas of interdisciplinary research.

Using mathematical modelling as the basis, computational methods provide procedures which, with the aid of the computer, allow complex problems to be solved. The techniques play an ever-increasing role in industry and there is further emphasis to apply the methodology to other important areas such as medicine and the life sciences.

The Zienkiewicz Centre for Computational Engineering, within which this course is run, has excellent computing facilities, including a state-of-the-art multi-processor super computer with virtual reality facilities and high-speed networking.

This Computer Modelling in Engineering course is suitable for those who are interested in gaining a solid understanding of computer modelling, specialising in either structures or fluids, and taking the skills gained through this course to develop their career in industry or research.

If you would like to qualify as a Chartered Engineer, this course is accredited with providing the additional educational components for the further learning needed to qualify as a Chartered Engineer, as set out by UK and European engineering professional institutions.

Modules

Modules on the Computer Modelling in Engineering programme typically include:

• Finite Element and Computational Analysis

• Numerical Methods for Partial Differential Equations

• Solid Mechanics

• Advanced Fluid Mechanics

• Dynamics and Transient Analysis

• Communication Skills for Research Engineers

• MRes Research Project

Accreditation

The MRes Computer Modelling in Engineering course is accredited by the Joint Board of Moderators (JBM).

The Joint Board of Moderators (JBM) is composed of the Institution of Civil Engineers (ICE), the Institution of Structural Engineers (IStructE), the Chartered Institution of Highways and Transportation (CIHT), and the Institute of Highway Engineers (IHE).

The MRes Computer Modelling in Engineering degree is accredited as meeting the requirements for Further Learning for a Chartered Engineer (CEng) for candidates who have already acquired an Accredited CEng (Partial) BEng(Hons) or an Accredited IEng (Full) BEng/BSc (Hons) undergraduate first degree.

The MRes Computer Modelling in Engineering degree has been accredited by the JBM under licence from the UK regulator, the Engineering Council.

Accreditation is a mark of assurance that the degree meets the standards set by the Engineering Council in the UK Standard for Professional Engineering Competence (UK-SPEC). An accredited degree will provide you with some or all of the underpinning knowledge, understanding and skills for eventual registration as an Incorporated (IEng) or Chartered Engineer (CEng). Some employers recruit preferentially from accredited degrees, and an accredited degree is likely to be recognised by other countries that are signatories to international accords.

Links with Industry

The Civil and Computational Engineering Centre has an extensive track record of industrial collaboration and contributes to many exciting projects, including the aerodynamics for the current World Land Speed Record car, Thrust SSC, and the future BLOODHOUND SSC, and the design of the double-decker super-jet Airbus A380.

Examples of recent collaborators and sponsoring agencies include: ABB, Audi, BAE Systems, British Gas, Cinpress, DERA, Dti, EADS, EPSRC, European Union, HEFCW, HSE, Hyder, Mobil, NASA, Quinshield, Rolls-Royce, South West Water, Sumitomo Shell, Unilever, US Army, WDA.

Student Quotes

“I was attracted to the MRes course at Swansea as the subject matter was just what I was looking for.

I previously worked as a Cardiovascular Research Assistant at the Murdoch Children’s Research Institute in Melbourne. My employer, the Head of the Cardiology Department, encouraged me to develop skills in modelling as this has a lot of potential to help answer some current questions and controversies in the field. I was looking for a Master’s level course that could provide me with computational modelling skills that I could apply to blood flow problems, particularly those arising from congenital heart disease.

The College of Engineering at Swansea is certainly a good choice. In the computational modelling area, it is one of the leading centres in the world (they wrote the textbook, literally). A lot of people I knew in Swansea initially came to study for a couple of years, but then ended up never leaving. I can see how that could happen.”

Jonathan Mynard, MRes Computer Modelling in Engineering, then PhD at the University of Melbourne, currently post-doctoral fellow at the Biomedical Simulation Laboratory, University of Toronto, Canada

Careers

Employment in a wide range of industries, which require the skills developed during the Computer Modelling in Engineering course, from aerospace to the medical sector. Computational modelling techniques have developed in importance to provide solutions to complex problems and as a graduate of this course, you will be able to utilise your highly sought-after skills in industry or research.

Research

The Research Excellence Framework (REF) 2014 ranks Engineering at Swansea as 10th in the UK for the combined score in research quality across the Engineering disciplines.

World-leading research

The REF shows that 94% of research produced by our academic staff is of World-Leading (4*) or Internationally Excellent (3*) quality. This has increased from 73% in the 2008 RAE.

Research pioneered at the College of Engineering harnesses the expertise of academic staff within the department. This ground-breaking multidisciplinary research informs our world-class teaching with several of our staff leaders in their fields.

Highlights of the Engineering results according to the General Engineering Unit of Assessment:

Research Environment at Swansea ranked 2nd in the UK

Research Impact ranked 10th in the UK

Research Power (3*/4* Equivalent staff) ranked 10th in the UK



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The University of Dundee has a long history of mathematical biology, going back to Professor Sir D'Arcy Wentworth Thompson, Chair of Natural History, 1884-1917. Read more

Mathematical Biology at Dundee

The University of Dundee has a long history of mathematical biology, going back to Professor Sir D'Arcy Wentworth Thompson, Chair of Natural History, 1884-1917. In his famous book On Growth and Form (where he applied geometric principles to morphological problems) Thompson declares:

"Cell and tissue, shell and bone, leaf and flower, are so many portions of matter, and it is in obedience to the laws of physics that their particles have been moved, molded and conformed. They are no exceptions to the rule that God always geometrizes. Their problems of form are in the first instance mathematical problems, their problems of growth are essentially physical problems, and the morphologist is, ipso facto, a student of physical science."

Current mathematical biology research in Dundee continues in the spirit of D'Arcy Thompson with the application of modern applied mathematics and computational modelling to a range of biological processes involving many different but inter-connected phenomena that occur at different spatial and temporal scales. Specific areas of application are to cancer growth and treatment, ecological models, fungal growth and biofilms. The overall common theme of all the mathematical biology research may be termed"multi-scale mathematical modelling" or, from a biological perspective, "quantitative systems biology" or"quantitative integrative biology".

The Mathematical Biology Research Group currently consists of Professor Mark Chaplain, Dr. Fordyce Davidson and Dr. Paul Macklin along with post-doctoral research assistants and PhD students. Professor Ping Lin provides expertise in the area of computational numerical analysis. The group will shortly be augmented by the arrival of a new Chair in Mathematical Biology (a joint Mathematics/Life Sciences appointment).

As a result, the students will benefit directly not only from the scientific expertise of the above internationally recognized researchers, but also through a wide-range of research activities such as journal clubs and research seminars.

Aims of the programme

1. To provide a Masters-level postgraduate education in the knowledge, skills and understanding of mathematical biology.
2. To enhance analytical and critical abilities and competence in the application of mathematical modeling techniques to problems in biomedicine.

Prramme Content

This one year course involves taking four taught modules in semester 1 (September-December), followed by a further 4 taught modules in semester 2 (January-May), and undertaking a project over the Summer (May-August).

A typical selection of taught modules would be:

Dynamical Systems
Computational Modelling
Statistics & Stochastic Models
Inverse Problems
Mathematical Oncology
Mathematical Ecology & Epidemiology
Mathematical Physiology
Personal Transferable Skills

Finally, all students will undertake a Personal Research Project under the supervision of a member of staff in the Mathematical Biology Research Group.

Methods of Teaching

The programme will involve a variety of teaching formats including lectures, tutorials, seminars, journal clubs, case studies, coursework, and an individual research project.

Taught sessions will be supported by individual reading and study.

Students will be guided to prepare their research project plan and to develop skills and competence in research including project management, critical thinking and problem solving, project reporting and presentation.

Career Prospects

The Biomedical Sciences are now recognizing the need for quantitative, predictive approaches to their traditional qualitative subject areas. Healthcare and Biotechnology are still fast-growing industries in UK, Europe and Worldwide. New start-up companies and large-scale government investment are also opening up employment prospects in emerging economies such as Singapore, China and India.

Students graduating from this programme would be very well placed to take advantage of these global opportunities.

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What do Facebook, the financial system, Internet or the brain have in common?. All are connected in a network. Read more
What do Facebook, the financial system, Internet or the brain have in common?

All are connected in a network. From the underlying skeleton of social relations, the interdependent evolution of our financial system, to the emergent collective computation in the brain, most of the complex systems that appear in society, technology, and nature are ultimately characterised by a nontrivial pattern of inter-relations. This underlying architecture is in turn shaping how information diffuses and spreads, how resilient the system is against attacks or perturbations, or how complex patterns emerge at the systemic level from the aggregation of seemingly simple individuals.

Our MSc in Network Science will provide a thorough grounding in the core principles of modelling and analysis of complex and networked systems, along with the principal analytical and numerical methodologies. This will open to students a host of career opportunities in systems and networks modelling industries, spanning the IT, financial, and biomedical sectors, that are now requiring such specialist knowledge and skills.

Network Science is a very active and rapidly evolving research field with high societal impact, which stands at the crossroads of graph theory, complexity and data analysis. Addressing the description and modelling of the architecture and dynamics of complex systems -systems composed by many interacting units that show collective behaviour- it stands as a new kind of science to cope with some of the most challenging endeavours we face today, in an ever increasingly more connected society.
Its impact and applications outside academia pervades technological sectors such as communications and infrastructures (Internet, transportation networks, energy networks, urban mobility), biostatistics and network biology (brain modelling, protein interaction networks, postgenomic era), public health (epidemic spreading models), marketing and IT (social media, data analytics) to cite a few. This specialist masters programme aims at providing graduate students and professionals with a rigorous training in the underlying mathematical concepts, the analysis and modelling of complex networks and networked systems, complemented with training in computing, numerical simulations and massive data analysis. It is aimed towards students whose undergraduate degree is in mathematics or a cognate discipline who wish to enter a career involving analysis and optimisation of diverse kinds of networks, networked dynamics and models.

Why study your MSc Network Science at Queen Mary?
This is a pioneering MSc in the UK, a joint programme, taught by our Schools of Mathematical Sciences, and Electronic Engineering and Computer Science, drawing on their strengths in research and teaching in the area of complex networks, mathematical modelling of complex systems, and data mining.

We teach what we know and what we do best. Within the School of Mathematics, the Complex Systems & Networks group is one of the biggest hubs in Network Science within the UK, where we address both fundamental and applied challenges in the mathematical modelling of complex systems with clear societal impact, in collaboration with several industrial stakeholders. Within the School of Electronic Engineering, the Networks group was founded in 1987, and has hugely expanded ever since, bringing their expertise in online social networks, data mining and cloud computing. The coalescence of both groups expertises has fostered the creation of this unique MSc.

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Visit our website for more information on fees, scholarships, postgraduate loans and other funding options to study Computer Modelling and Finite Elements in Engineering Mechanics at Swansea University - 'Welsh University of the Year 2017' (Times and Sunday Times Good University Guide 2017). Read more

Visit our website for more information on fees, scholarships, postgraduate loans and other funding options to study Computer Modelling and Finite Elements in Engineering Mechanics at Swansea University - 'Welsh University of the Year 2017' (Times and Sunday Times Good University Guide 2017).

Swansea University has been at the forefront of international research in the area of computational engineering. Internationally renowned engineers at Swansea pioneered the development of numerical techniques, such as the finite element method, and associated computational procedures that have enabled the solution of many complex engineering problems. As a student on the Master's course in Computer Modelling and Finite Elements in Engineering Mechanics, you will find the course utilises the expertise of academic staff to provide high-quality postgraduate training.

Key Features: Computer Modelling and Finite Elements in Engineering Mechanics

Computer simulation is now an established discipline that has an important role to play in engineering, science and in newly emerging areas of interdisciplinary research.

Using mathematical modelling as the basis, computational methods provide procedures which, with the aid of the computer, allow complex problems to be solved. The techniques play an ever-increasing role in industry and there is further emphasis to apply the methodology to other important areas such as medicine and the life sciences.

This Computer Modelling and Finite Elements in Engineering Mechanics course provides a solid foundation in computer modelling and the finite element method in particular.

The Zienkiewicz Centre for Computational Engineering, within which this course is run, has excellent computing facilities, including a state-of-the-art multi-processor super computer with virtual reality facilities and high-speed networking.

Modules

Modules on the Computer Modelling and Finite Elements in Engineering Mechanics course can vary each year but you could expect to study:

Reservoir Modelling and Simulation

Solid Mechanics

Finite Element Computational Analysis

Advanced Fluid Mechanics

Computational Plasticity

Fluid-Structure Interaction

Nonlinear Continuum Mechanics

Computational Fluid Dynamics

Dynamics and Transient Analysis

Computational Case Study

Communication Skills for Research Engineers

Numerical Methods for Partial Differential Equations

Accreditation

The MSc Computer Modelling and Finite Elements in Engineering Mechanics course is accredited by the Joint Board of Moderators (JBM).

The Joint Board of Moderators (JBM) is composed of the Institution of Civil Engineers (ICE), the Institution of Structural Engineers (IStructE), the Chartered Institution of Highways and Transportation (CIHT), and the Institute of Highway Engineers (IHE).

The MSc Computer Modelling and Finite Elements in Engineering Mechanics degree is accredited as meeting the requirements for Further Learning for a Chartered Engineer (CEng) for candidates who have already acquired an Accredited CEng (Partial) BEng(Hons) or an Accredited IEng (Full) BEng/BSc (Hons) undergraduate first degree.

The MSc Computer Modelling and Finite Elements in Engineering Mechanics degree has been accredited by the JBM under licence from the UK regulator, the Engineering Council.

Accreditation is a mark of assurance that the degree meets the standards set by the Engineering Council in the UK Standard for Professional Engineering Competence (UK-SPEC). An accredited degree will provide you with some or all of the underpinning knowledge, understanding and skills for eventual registration as an Incorporated (IEng) or Chartered Engineer (CEng). Some employers recruit preferentially from accredited degrees, and an accredited degree is likely to be recognised by other countries that are signatories to international accords.

Facilities

Our new home at the innovative Bay Campus provides some of the best university facilities in the UK, in an outstanding location.

Hardware includes a 450 cpu Cluster, high-end graphics workstations and high-speed network links. Extensive software packages include both in-house developed and 'off-the-shelf' commercial.

Links with Industry

The Zienkiewicz Centre for Computational Engineering has an extensive track record of industrial collaboration and contributes to many exciting projects, including the aerodynamics for the current World Land Speed Record car, Thrust SSC, and the future BLOODHOUND SSC, and the design of the double-decker super-jet Airbus A380.

Careers

Employment in a wide range of industries, which require the skills developed during the Computer Modelling and Finite Elements in Engineering Mechanics course, from aerospace to the medical sector. Computational modelling techniques have developed in importance to provide solutions to complex problems and as a graduate of this course in Computer Modelling and Finite Elements in Engineering Mechanics, you will be able to utilise your highly sought-after skills in industry or research.

Research

The Research Excellence Framework (REF) 2014 ranks Engineering at Swansea as 10th in the UK for the combined score in research quality across the Engineering disciplines.

The REF assesses the quality of research in the UK Higher Education sector, assuring us of the standards we strive for.

World-Leading Research

The REF shows that 94% of research produced by our academic staff is of World-Leading (4*) or Internationally Excellent (3*) quality. This has increased from 73% in the 2008 RAE.

Research pioneered at the College of Engineering harnesses the expertise of academic staff within the department. This ground-breaking multidisciplinary research informs our world-class teaching with several of our staff leaders in their fields.



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Financial Mathematics is a branch of Mathematics where advanced mathematical and statistical methods are developed for and applied to financial markets and financial management. Read more

Overview

Financial Mathematics is a branch of Mathematics where advanced mathematical and statistical methods are developed for and applied to financial markets and financial management. Its main aims are to quantify and hedge risks in the financial marketplace.

Effective computational methods are crucial for the successful use of mathematical modelling in finance. The MSc in Financial and Computational Mathematics is designed to reflect this combination of knowledge and skills so that its graduates are well equipped to enter the competitive job markets of quantitative finance and related fields.

The course is focused on computational techniques and mathematical modelling used in the financial industry and on the required background in finance. The course is provided by the School of Mathematical Sciences with valuable input from the School of Economics. To ensure that the degree keeps pace with changes in employer expectations and employment opportunities, the course has its own advisory board which consists of leading experts from the financial industry and academia.

Key facts:

- The School of Mathematical Sciences is one of the largest and strongest mathematics departments in the UK, with over 60 full-time academic staff.
- In the latest independent Research Assessment Exercise, the school ranked 8th in the UK in terms of research power across the three subject areas within the School of Mathematical Sciences (pure mathematics, applied mathematics, statistics and operational research).
- In the last independent Teaching Quality Assessment, the School scored 23 out of 24.
- The course has its own advisory board (see below) consisting of leading experts from the financial industry and academia.
- The course is offered in collaboration with the School of Economics.

Module details

Core modules include: financial mathematics, advanced financial mathematics, scientific computing and c++, advanced scientific computing, financial mathematics dissertation.

Optional Stream 1 (Maths/Stats and Computing): Optimisation, Time Series and Forecasting, Statistical Foundations.

Optional Stream 2A: Econometic Theory, Financial and Macro Econometrics, Time Series Econometrics, Mathematics for Engineering Management, Game Theory.

Optional Stream 2B: Microeconomic Analysis, Financial Economics, Options and Futures Markets, Mathematics for Engineering Management, Game Theory.

English language requirements for international students

IELTS: 6.5 (with no less than 6.0 in any element)

Further information



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