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Masters Degrees (Numerical Methods)

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If you have a mathematical background and want to apply your mathematical skills to understanding the complex behaviour of the Earth’s atmosphere and oceans then this could be the programme for you. Read more

If you have a mathematical background and want to apply your mathematical skills to understanding the complex behaviour of the Earth’s atmosphere and oceans then this could be the programme for you. This is an exciting interdisciplinary subject, of increasing importance to a society facing climate change.

You’ll be trained in both modern applied mathematics and atmosphere-ocean science, combining teaching resources from the School of Mathematics and the School of Earth and Environment. The latter are provided by members of the School’s Institute for Climate and Atmospheric Science, part of the National Centre for Atmospheric Science.

Only a handful of UK universities are positioned to offer similar interdisciplinary training in modern applied mathematics and atmosphere-ocean-climate science.

If you do not meet the full academic entry requirements then you may wish to consider the Graduate Diploma in Mathematics. This course is aimed at students who would like to study for a mathematics related MSc course but do not currently meet the entry requirements. Upon completion of the Graduate Diploma, students who meet the required performance level will be eligible for entry onto a number of related MSc courses, in the following academic year.

Course content

The focus of the course is on analysing the equations of fluid dynamics and thermodynamics, via mathematical and numerical modelling. The programme is highly flexible, meaning you are free to choose options from applied maths, atmosphere-ocean science, numerical methods and scientific computation alongside the compulsory core applied maths and fluid dynamics modules.

Topics are drawn from four broad areas:

  1. Applied mathematics: asymptotic methods, fluid dynamics, mathematical theory of waves and stability of flow
  2. Numerical methods and computing: discretization of ordinary and partial differential equations, algorithms for linear algebra, direct use of numerical weather and climate models
  3. Atmospheric dynamics: structure of the atmosphere, dynamics of weather systems and atmospheric waves
  4. Ocean dynamics: the large-scale ocean circulation, surface waves and tides

Modules are taught either by the School of Mathematics or the School of Earth and Environment.

The course is made up of two parts: a set of taught modules, and a research project. Two-thirds of the course consists of taught modules involving lectures and some computer workshops. Beyond a compulsory core of atmosphere-ocean fluid dynamics, students may choose options to suit their interests from applied maths (e.g. nonlinear dynamics), atmosphere-ocean science (e.g. climate change processes, weather forecasting), numerical methods and scientific computation. The final third of the course consists of an intensive summer project, in which students conduct an in-depth investigation of a chosen subject related to the course.

Course structure

Compulsory modules

  • Dissertation in Mathematics 60 credits

Optional modules

  • Scientific Computation 15 credits
  • Mathematical Methods 15 credits
  • Linear and Non-Linear Waves 15 credits
  • Hydrodynamic Stability 15 credits
  • Dynamical Systems 15 credits
  • Nonlinear Dynamics 15 credits
  • Analytic Solutions of Partial Differential Equations 15 credits
  • Introduction to Entropy in the Physical World 15 credits
  • Astrophysical Fluid Dynamics 15 credits
  • Numerical Methods 10 credits
  • Modern Numerical Methods 15 credits
  • Fluid Dynamics 2 15 credits
  • Advanced Mathematical Methods 20 credits
  • Advanced Linear and Nonlinear Waves 20 credits
  • Advanced Hydrodynamic Stability 20 credits
  • Advanced Dynamical Systems 20 credits
  • Advanced Nonlinear Dynamics 20 credits
  • Advanced Entropy in the Physical World 20 credits
  • Foundations of Fluid Dynamics 30 credits
  • Advanced Geophysical Fluid Dynamics 20 credits
  • Advanced Astrophysical Fluid Dynamics 20 credits
  • Advanced Modern Numerical Methods 20 credits
  • Independent Learning and Skills Project 15 credits
  • Atmosphere and Ocean Climate Change Processes 10 credits
  • Practical Weather Forecasting 10 credits
  • Dynamics of Weather Systems 15 credits
  • Weather, Climate and Air Quality 30 credits
  • Environmental Modelling 15 credits
  • Advanced Atmosphere and Ocean Dynamics 15 credits

For more information on typical modules, read Atmosphere-Ocean Dynamics MSc in the course catalogue

Learning and teaching

Teaching is by lectures, tutorials, practical classes, and one-on-one supervision (for research projects). Outside these formal sessions, students are able to study at their own pace, aided by our wide range of electronic teaching resources.

Assessment

Assessment is by course work and written exams which take place at the end of the semester in which the module is taught.

Career opportunities

Students will be prepared for postgraduate research in applied mathematics or atmosphere-ocean science, or employment in the environmental sector.

However, given the interdisciplinary nature of the programme, graduates will have expertise and skills in a number of different areas, and should be attractive to a wide range of employers.

Careers support

We encourage you to prepare for your career from day one. That’s one of the reasons Leeds graduates are so sought after by employers.

The Careers Centre and staff in your faculty provide a range of help and advice to help you plan your career and make well-informed decisions along the way, even after you graduate. Find out more at the Careers website.



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Scientists and engineers are tackling ever more complex problems, most of which do not admit analytical solutions and must be solved numerically. Read more
Scientists and engineers are tackling ever more complex problems, most of which do not admit analytical solutions and must be solved numerically. Numerical methods can only play an even more important role in the future as we face even bigger challenges. Therefore, skilled scientific programmers are in high demand in industry and academia and will drive forward much of the future economy.

Degree information

This programme aims to produce highly computationally skilled scientists and engineers capable of applying numerical methods and critical evaluation of their results to their field of science or engineering. It brings together best practice in computing with cutting-edge science and provides a computing edge over traditional science, engineering and mathematics programmes.

Students undertake modules to the value of 180 credits.

The programme consists of six core modules (90 credits), two optional modules (30 credits) and a dissertation/report (60 credits). A Postgraduate Diploma, six core modules (90 credits), two optional modules (30 credits), is also offered.

Core modules
-Computational and Simulation Methods
-Numerical Methods
-Numerical Optimisation
-Research Computing with C++
-Research Software Engineering with Python
-Techniques of High-Performance

Optional modules - options include a wide selection of modules across UCL Engineering and UCL Mathematical & Physical Sciences.

Dissertation/report
All students undertake an independent research project project which culminates in a dissertation of 20,000 words.

Teaching and learning
The programme is delivered through a combination of lectures and hands-on programming and includes a variety of short programming projects, delivered as part of the taught component. Students are encouraged to participate in scientific seminars, for example, weekly seminars at the UCL Centre for Inverse Problems. Assessment is through examinations, assignments, small projects and the dissertation, including a computer programme.

Careers

We expect our graduates to take up exciting science and engineering roles in industry and academia with excellent prospects for professional development and steep career advancement opportunities. This degree enable students to work on cutting-edge real-life problems, overcome the challenges they pose and so contribute to advancing knowledge and technology in our society.

Employability
Students develop a comprehensive set of skills which are in high demand both in industry and academia: professional software development skills including state-of-the-art scripting and compiled languages; knowledge of techniques used in high-performance computing; understanding and an ability to apply a wide range of numerical methods and numerical optimisation; a deeper knowledge of their chosen science subject; oral and written presentational skills.

Why study this degree at UCL?

UCL has a global reputation for excellence in research and is committed to delivering impact and innovations that enhance the lives of people in the UK, across Europe and around the world. UCL is consistently placed in the global top 20 across a wide range of university rankings (currently fifth in QS World University Rankings 2014/15). Furthermore, the Thomson Scientific Citation Index shows that UCL is the 2nd most highly cited European university and 13th in the world.

Our wide-ranging expertise provides opportunities for groundbreaking interdisciplinary investigation. World-leading experts in the field and students benefit from a programme of distinguished visitors and guest speakers in many scientific seminars. In this way a network of collaborators, mentors and peers is created, which students can access in their future career.

This degree has been designed to balance a professional software development and high performance computing skills with a comprehensive selection of numerical mathematics and scientific subjects, culminating in a scientific computing dissertation project. The dual aspect of a science and computing degree enable students to tackle real-life problems in a structured and rigorous way and produce professional software for their efficient solution.

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The course provides you with a strong mathematical background with the skills necessary to apply your expertise to the solution of real finance problems. … Read more

The course provides you with a strong mathematical background with the skills necessary to apply your expertise to the solution of real finance problems. You will develop skills so that you are able to formulate a well posed problem from a description in financial language, carry out relevant mathematical analysis, develop and implement an appropriate numerical scheme and present and interpret these results.

The course lays the foundation for further research in academia or for a career as a quantitative analyst in a financial or other institution.

You will take three introductory courses in the first week. The introductory courses cover partial differential equations, probability and statistics and MATLAB.

The first term focuses on compulsory core material, offering 80 hours of lectures and 40 hours of classes/practical. The core courses are as follows:

  • Stochastic Calculus
  • Financial Derivatives
  • Numerical Methods I - Monte-Carlo
  • Numerical Methods I - Finite Differences
  • Statistics and Financial Data Analysis
  • Financial Programming with C++ 1

In the second term, three streams are offered; each stream consists of 32 hours of lectures and 16 hours of classes/practical. The Tools stream is mandatory and you will also take either the Modelling stream or the Data-driven stream.

Modelling stream

  • Exotic derivatives
  • Stochastic volatility, jump diffusions
  • Commodities
  • Fixed income

Data-driven stream

  • Asset pricing and inefficiency of markets
  • Market microstructure and trading
  • Algorithmic trading
  • Advanced financial data analysis
  • Machine learning
  • Python

Tools stream

  • Numerical methods 2 - Monte Carlo methods
  • Numerical methods 2 - Finite differences
  • Calibration
  • Optimisation
  • Introduction to stochastic control

As well as the streams, the course includes a compulsory one-week (24 hours of lectures) intensive module on quantitative risk management which is to be held in/around the week before the third term.

The third term is dedicated to a dissertation project which is to be written on a topic chosen in consultation with your supervisor.

The second component of the financial computing course, Financial Computing with C++ 2 (24 hours of lectures and practicals in total), is held shortly after the third term.

The examination will consist of the following elements:

  • two written examinations and one take-home project, each of two hours' duration - the written examinations will cover the core courses in mathematical methods and numerical analysis
  • a written examination on the Modelling stream or a written examination and a computer-based practical examination on the Data-driven stream
  • a written examination assessing the Tools stream
  • a take-home project assessing the course in quantitative risk management
  • two practical examinations assessing two courses in financial computing with C++.

Graduate destinations

MSc graduates have been recruited by prominent investment banks and hedge funds. Many past students have also progressed to PhD-level studies at leading universities in Europe and elsewhere.



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The MPhil programme in Scientific Computing is a full-time 12-month course which aims to provide education of the highest quality at Master’s level. Read more
The MPhil programme in Scientific Computing is a full-time 12-month course which aims to provide education of the highest quality at Master’s level. Covering topics of high-performance scientific computing and advanced numerical methods and techniques, it produces graduates with rigorous research and analytical skills, who are well-equipped to proceed to doctoral research or directly into employment in industry, the professions, and the public service. It also provides training for the academic researchers and teachers of the future, encouraging the pursuit of research in computational methods for science and technology disciplines, thus being an important gateway for entering PhD programmes containing a substantial component of computational modelling.

See the website http://www.graduate.study.cam.ac.uk/courses/directory/pcphmpscm

Course detail

The MPhil in Scientific Computing has a research and a taught element. The research element is a project on a science or technology topic which is studied by means of scientific computation. The taught element comprises of core lecture courses on topics of scientific computing and elective lecture courses relevant to the science or technology topic of the project. Most of the projects are expected to make use of the University’s High Performance Computing Service.

The students will attend lecture courses during Michaelmas Term (some courses may be during Lent Term) and then they will undertake a substantial Research Project over the next 6 months (from March to the end of August) in a participating Department. The research element aims to provide essential skills for continuation to a PhD programme or employment, as well as to assess and enhance the research capacity of the students. It is based on a science or technology topic which is studied by means of scientific computation. Research project topics will be provided by academic supervisors or by the industrial partners who are working with the participating Departments and may be sponsoring the research project.

There is equal examination credit weighting between the taught and the research elements of the course, which is gained by submitting a dissertation on the project and by written assignments and examinations on the core and elective courses, respectively.

Weighting of the assessed course components is as follows: Dissertation (research) 50%; written assignments on the core courses 25%; written examinations on the elective courses 25%.

Learning Outcomes

By the end of the course, students will have:

- a comprehensive understanding of numerical methods, and a thorough knowledge of the literature, applicable to their own research;
- demonstrated originality in the application of knowledge, together with a practical understanding of how research and enquiry are used to create and interpret knowledge in their field;
- shown abilities in the critical evaluation of current research and research techniques and methodologies;
- demonstrated self-direction and originality in tackling and solving problems, and acted autonomously in the planning and implementation of research.

Format

The taught element comprises core lecture courses on topics of all aspects of scientific computing, and elective lecture courses relevant to the topic of the research project.

The taught element comprises core lecture courses on topics of all aspects of scientific computing, and elective lecture courses relevant to the topic of the research project. There is equal examination credit weighting between the taught and the research elements of the course, which is gained by submitting a dissertation on the project and by written assignments and examinations on the core and elective courses, respectively. Weighting of the assessed course components is as follows: Dissertation (research) 50%; written assignments 25%; written examinations 25%.

The core lectures are on topics of high performance scientific computing numerical analysis and advanced numerical methods and techniques. They are organized by the Centre for Scientific Computing and are taught and examined during the first five months (October-February). Their purpose is to provide the students with essential background knowledge for completing their dissertation and for their general education in scientific computing.

In particular, their objective is to introduce students to the simulation science pipeline of problem identification, modelling, simulation and evaluation - all from the perspective of employing high-performance computing. Numerical discretisation of mathematical models will be a priority, with a specific emphasis on understanding the trade-offs (in terms of modelling time, pre-processing time, computational time, and post-processing time) that must be made when solving realistic science and engineering problems. Understanding and working with computational methods and parallel computing will be a high priority. To help the students understand the material, the lecturers will furnish the courses with practical coursework assignments.

The lectures on topics of numerical analysis and HPC are complemented with hands-on practicals using Linux-based laptops provided by the course (students may bring their own), as well as on the University’s High Performance Computing Service.

Appropriate elective lecture courses are selected from Master’s-level courses offered by the Departments of the School of Physical Sciences, Technology or Biological Sciences. The choice of courses will be such as to provide the students with essential background knowledge for completing their theses and for their general education in the materials science application of the project. They are decided in consultation with the project supervisor. While every effort is made within the Departments to arrange the timetable in a coherent fashion, it is inevitable that some combinations of courses will be ruled out by their schedule, particularly if the choices span more than one department.

Continuing

For continuation to a PhD programme in Scientific Computing, students are required to gain a Distinction (overall grade equal or greater than 75%).

How to apply: http://www.graduate.study.cam.ac.uk/applying

Funding Opportunities

There are no specific funding opportunities advertised for this course. For information on more general funding opportunities, please follow the link below.

General Funding Opportunities http://www.graduate.study.cam.ac.uk/finance/funding

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Summary. The Aerodynamics and Computation programme looks at the fundamentals of aerodynamics as a subject, focusing on numerical methods and the physics and computation of turbulence. Read more

Summary

The Aerodynamics and Computation programme looks at the fundamentals of aerodynamics as a subject, focusing on numerical methods and the physics and computation of turbulence.

Suitable for those from an engineering, physical sciences or mathematics background who are aiming for advanced specialisation in aerodynamics.

Modules

Compulsory modules include: Aerothermodynamics; Advanced Computational Methods I; Applications of CFD; Turbulence: Physics and Modelling; MSc Research Project.

Optional modules: four from: Advanced Computational Methods II, Aeroacoustics; Biological Flow; Design, Search and Optimisation; Experimental Methods for Aerodynamics; Flow Control; Hypersonic and High Temperature Gas Dynamics; Race Car Aerodynamics; Wing Aerodynamics; Numerical Methods

Visit our website for further information.



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

Swansea University has gained a significant international profile as one of the key international centres for research and training in computational mechanics and engineering. As a student on the Master's course in Erasmus Mundus Computational Mechanics, you will be provided with in-depth, multidisciplinary training in the application of the finite element method and related state-of-the-art numerical and computational techniques to the solution and simulation of highly challenging problems in engineering analysis and design.

Key Features of Erasmus Mundus Computational Mechanics MSc

The Zienkiewicz Centre for Computational Engineering is acknowledged internationally as the leading UK centre for computational engineering research. It represents an interdisciplinary group of researchers who are active in computational or applied mechanics. It is unrivalled concentration of knowledge and expertise in this field. Many numerical techniques currently in use in commercial simulation software have originated from Swansea University.

The Erasmus Mundus MSc Computational Mechanics course is a two-year postgraduate programme run by an international consortium of four leading European Universities, namely Swansea University, Universitat Politècnica de Catalunya (Spain), École Centrale de Nantes (France) and University of Stuttgart (Germany) in cooperation with the International Centre for Numerical Methods in Engineering (CIMNE, Spain).

As a student on the Erasmus Mundus MSc Computational Mechanics course, you will gain a general knowledge of the theory of computational mechanics, including the strengths and weaknesses of the approach, appreciate the worth of undertaking a computational simulation in an industrial context, and be provided with training in the development of new software for the improved simulation of current engineering problems.

In the first year of the Erasmus Mundus MSc Computational Mechanics course, you will follow an agreed common set of core modules leading to common examinations in Swansea or Barcelona. In addition, an industrial placement will take place during this year, where you will have the opportunity to be exposed to the use of computational mechanics within an industrial context. For the second year of the Erasmus Mundus MSc Computational Mechanics, you will move to one of the other Universities, depending upon your preferred specialisation, to complete a series of taught modules and the research thesis. There will be a wide choice of specialisation areas (i.e. fluids, structures, aerospace, biomedical) by incorporating modules from the four Universities. This allows you to experience postgraduate education in more than one European institution.

Modules

Modules on the Erasmus Mundus MSc Computational Mechanics course can vary each year but you could expect to study the following core modules (together with elective modules):

Numerical Methods for Partial Differential Equations

Continuum Mechanics

Advanced Fluid Mechanics

Industrial Project

Finite Element Computational Analysis

Entrepreneurship for Engineers

Finite Element in Fluids

Computational Plasticity

Fluid-Structure Interaction

Nonlinear Continuum Mechanics

Computational Fluid Dynamics

Dynamics and Transient Analysis

Reservoir Modelling and Simulation

Accreditation

The Erasmus Mundus Computational 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).

This 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.

See http://www.jbm.org.uk for further information.

This 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

On the Erasmus Mundus MSc Computational Mechanics course, you will have the opportunity to apply your skills and knowledge in computational mechanics in an industrial context.

As a student on the Erasmus Mundus MSc Computational Mechanics course you will be placed in engineering industries, consultancies or research institutions that have an interest and expertise in computational mechanics. Typically, you will be trained by the relevant industry in the use of their in-house or commercial computational mechanics software.

You will also gain knowledge and expertise on the use of the particular range of commercial software used in the industry where you are placed.

Careers

The next decade will experience an explosive growth in the demand for accurate and reliable numerical simulation and optimisation of engineering systems.

Computational mechanics will become even more multidisciplinary than in the past and many technological tools will be, for instance, integrated to explore biological systems and submicron devices. This will have a major impact in our everyday lives.

Employment can be found in a broad range of engineering industries as this course provides the skills for the modelling, formulation, analysis and implementation of simulation tools for advanced engineering problems.

Student Quotes

“I gained immensely from the high quality coursework, extensive research support, confluence of cultures and unforgettable friendship.”

Prabhu Muthuganeisan, MSc Computational Mechanics



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The Masters in Mathematics/Applied Mathematics offers courses, taught by experts, across a wide range. Mathematics is highly developed yet continually growing, providing new insights and applications. Read more
The Masters in Mathematics/Applied Mathematics offers courses, taught by experts, across a wide range. Mathematics is highly developed yet continually growing, providing new insights and applications. It is the medium for expressing knowledge about many physical phenomena and is concerned with patterns, systems, and structures unrestricted by any specific application, but also allows for applications across many disciplines.

Why this programme

◾Mathematics at the University of Glasgow is ranked 3rd in Scotland (Complete University Guide 2017).
◾The School has a strong international reputation in pure and applied mathematics research and our PGT programmes in Mathematics offer a large range of courses ranging from pure algebra and analysis to courses on mathematical biology and fluids.
◾You will be taught by experts across a wide range of pure and applied mathematics and you will develop a mature understanding of fundamental theories and analytical skills applicable to many situations.
◾You will participate in an extensive and varied seminar programme, are taught by internationally renowned lecturers and experience a wide variety of projects.
◾Our students graduate with a varied skill set, including core professional skills, and a portfolio of substantive applied and practical work.

Programme structure

Modes of delivery of the Masters in Mathematics/Applied Mathematics include lectures, laboratory classes, seminars and tutorials and allow students the opportunity to take part in project work.

If you are studying for the MSc you will take a total of 120 credits from a mixture of Level-4 Honours courses, Level-M courses and courses delivered by the Scottish Mathematical Sciences Training Centre (SMSTC).

You will take courses worth a minimum of 90 credits from Level-M courses and those delivered by the SMSTC. The remaining 30 credits may be chosen from final-year Level-H courses. The Level-M courses offered in a particular session will depend on student demand. Below are courses currently offered at these levels, but the options may vary from year to year.

Level-H courses (10 or 20 credits)
◾Algebraic & geometric topology
◾Continuum mechanics & elasticity
◾Differential geometry
◾Fluid mechanics
◾Functional analysis
◾Further complex analysis
◾Galois theory
◾Mathematical biology
◾Mathematical physics
◾Numerical methods
◾Number theory
◾Partial differential equations
◾Topics in algebra.

Level-M courses (20 credits)
◾Advanced algebraic & geometric topology
◾Advanced differential geometry & topology
◾Advanced functional analysis
◾Advanced methods in differential equations
◾Advanced numerical methods
◾Biological & physiological fluid mechanics
◾Commutative algebra & algebraic geometry
◾Elasticity
◾Further topics in group theory
◾Lie groups, lie algebras & their representations
◾Magnetohydrodynamics
◾Operator algebras
◾Solitons
◾Special relativity & classical field theory.

SMSTC courses (20 credits)
◾Advanced Functional Analysis
◾Advanced Mathematical Methods

The project titles are offered each year by academic staff and so change annually.

Career prospects

Career opportunities are diverse and varied and include academia, teaching, industry and finance.

Graduates of this programme have gone on to positions such as:
Maths Tutor at a university.

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This interdisciplinary Masters degree provides you with a broad background in some mainstream and modern aspects of mathematics and computer science. Read more

This interdisciplinary Masters degree provides you with a broad background in some mainstream and modern aspects of mathematics and computer science. You’ll be introduced to sophisticated techniques at the forefront of both disciplines.

The programme combines teaching and research from the School of Mathematics and the School of Computing. Based on the Schools’ complementary research strengths the programme follows two main strands:

  • Algorithms and complexity theory
  • Numerical methods and parallel computing

You’ll have the choice to specialise in one of these strands, gaining specialist knowledge and skills that will prepare you for a wide range of careers. You’ll also develop your research skills when you complete your dissertation.

If you do not meet the full academic entry requirements then you may wish to consider the Graduate Diploma in Mathematics. This course is aimed at students who would like to study for a mathematics related MSc course but do not currently meet the entry requirements. Upon completion of the Graduate Diploma, students who meet the required performance level will be eligible for entry onto a number of related MSc courses, in the following academic year.

Course content

It is expected that you will specialise in one of two areas during the course, although this is not essential.

The two strands are:

Algorithms and complexity theory and connections to logic and combinatorics

This concerns the efficiency of algorithms for solving computational problems, and identifies hierarchies of computational difficulty. This subject has applications in many areas, such as distributed computing, algorithmic tools to manage transport infrastructure, health informatics, artificial intelligence, and computational biology.

Numerical methods and parallel computing

Many problems, in mathematics, physics, astrophysics and biology cannot be solved using analytical techniques and require the application of numerical algorithms for progress. The development and optimisation of these algorithms coupled to the recent increase in computing power via the availability of massively parallel machines has led to great advances in many fields of computational mathematics. This subject has applications in many areas, such as combustion, lubrication, atmospheric dispersion, river and harbour flows, and many more.

For information on typical modules, read Mathematics and Computer Science MSc in the course catalogue

Learning and teaching

Teaching is carried out through a mixture of lectures and smaller group activities such as workshops. Most modules are assessed by a mix of coursework and written examinations. There is also the opportunity to complete a summer project which is individually supervised by a member of staff.

Assessment

The taught course is primarily assessed by end-of-semester examinations with a small component of continuous assessment. The semester three project is assessed by a written dissertation and a short oral presentation.

Career opportunities

Each of these areas offers many career options, and the MSc will provide you with both technical and transferrable skills, for example, conducting an extended and independent research project. It will also offer you excellent preparation for doctoral research in these or related subjects. On completion of the degree you can progress onto a wide range of opportunities including:

  • PhD in Mathematics, or in Computer Science
  • Careers in Computing and Industries which require algorithmic tools (transport infrastructure, health informatics, computational biology, artificial intelligence, companies developing the internet (e.g. search engines).
  • Many other careers (e.g. in Finance) where a mathematics background is valued.

In collaboration with both industrial and academic partners, our research has resulted in computational techniques, and software, that has been widely applied. Our industry links are extensive and include companies such as Google, Yahoo, Akamai, Microsoft, and Tracsis, as well as the NHS.

Careers support

We encourage you to prepare for your career from day one. That’s one of the reasons Leeds graduates are so sought after by employers.

The Careers Centre and staff in your faculty provide a range of help and advice to help you plan your career and make well-informed decisions along the way, even after you graduate. Find out more at the Careers website.



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There is a growing need for qualified professionals with expertise in environmental modelling. The UCL Environmental Modelling MSc is a cross-disciplinary degree that provides rigorous technical and scientific training for the next generation of environmental modelling professionals. Read more
There is a growing need for qualified professionals with expertise in environmental modelling. The UCL Environmental Modelling MSc is a cross-disciplinary degree that provides rigorous technical and scientific training for the next generation of environmental modelling professionals.

Degree information

You will gain a well-rounded training in the role, implementation and application of models in environmental science. Core modules provide a critical perspective on model-based science, and introduce essential computational and numerical methods. The programme is contextualised with reference to the challenges of understanding both natural and human-induced changes to a variety of environmental systems.

Students take modules to the value of 180 credits. The programme consists of four core modules (60 credits), optional modules (60 credits) and a research dissertation (60 credits). A Postgraduate Diploma (120 credits, full-time nine months, part-time two years) is offered. A Postgraduate Certificate (60 credits, full-time 12 weeks, part-time one year) is offered.

Core modules
-Models in Environmental Science
-Global Environmental Change
-Scientific Computing
-Analytical and Numerical Methods

Optional modules - options may include:
-Climate Modelling
-Coastal Change
-Environmental GIS
-Impacts of Climate Change on Hydro-Ecological Systems
-Lakes
-Ocean Circulation and Climate Change
-Surface Water Modelling
-Terrestrial Carbon: Monitoring and Modelling
-Other MSc modules offered across UCL may be taken at the discretion of the MSc convenor

Dissertation/report
All students undertake an independent research project, culminating in a dissertation of approximately 12,000 words and an oral presentation.

Teaching and learning
The programme is delivered through a combination of lectures, seminars, tutorials, and laboratory and computer-based practical classes. Assessment is through independent project work, practical-based and written coursework, written examinations and the dissertation.

Careers

The programme has been designed to provide an ideal foundation for PhD research, or for employment with environmental monitoring and protection agencies, industry and environmental consultancies. Graduates have gone on to careers as management consultants, business analysts and university researchers.

Top career destinations for this degree:
-Research Fellow, University of Girona and studying PhD Sanitas, Universitat de Girona (University of Girona)
-Risk Analyst, Canopius

Employability
Modelling was identified as the highest priority UK skills gap in a government review of the environmental sector. This MSc programme exposes students to the full range of environmental modelling which places graduates in a strong position to find employment. We anticipate that graduates of this MSc are either employed in the private environmental consulting sector or undertake a PhD.

Why study this degree at UCL?

The Environmental Modelling MSc is run by UCL Geography which enjoys an outstanding international reputation for its research and teaching. Research groups contributing to the MSc include those concerned with environmental modelling and observation, past climates, and recent environmental change and biodiversity.

The programme draws on the unrivalled strengths of UCL in environment modelling. Our expertise encompasses state-of-the-art global climate models, regional ocean models, advanced hydrodynamic and hydrological simulations, palaeoclimate reconstruction over geological to recent historical timescales, earth observation-derived vegetation and carbon cycle modelling, and model-based assessment of climate change impacts on coastal, estuarine and freshwater systems.

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The ICMA Centre’s financial engineering degree is highly respected by quantitative analysts and their employers. The credit crunch and subsequent events have emphasised the need to develop better pricing and better hedging models for all complex products. Read more

The ICMA Centre’s financial engineering degree is highly respected by quantitative analysts and their employers. The credit crunch and subsequent events have emphasised the need to develop better pricing and better hedging models for all complex products. The practical and quantitative skills that you will develop on the programme will equip you to meet this challenge.

Our compulsory modules provide a firm grounding in probability theory, stochastic calculus, derivatives pricing, quantitative and numerical methods, structuring products, volatility analysis, and the modelling of credit, equity, foreign exchange and interest rate derivatives. We also provide a thorough training in C++ and other programming tools.

Optional modules will allow you to focus on risk analysis, portfolio management, designing trading strategies or econometric analysis. This newly structured degree aims to further enhance the strong reputation of its precursor – the MSc in Financial Engineering and Quantitative Analysis, which was established back in 1999. A good background in mathematics is required for acceptance to this programme (see entry requirements below).

Highlights

  • A highly technical programme for those with strong mathematical skills
  • Gain knowledge of derivatives pricing tools and methods, as well as the use of programming languages like C++ and VBA
  • Designed with the support of industry practitioners to equip students with the skills and knowledge needed to succeed
  • Graduates are able to make an early contribution through the unique combination of hands-on, practical skills and the necessary underlying finance theory
  • Benefit from the combined expertise of both the ICMA Centre and the Department of Mathematics

Course structure

October – December: Part 1 Autumn Term

January: Part 1 Exams

January-April: Part 2 Spring Term

May – June: Part 2 Exams

June – August (12 month programme only): Part 3

August/Sep (12 month programme only): Part 3 Coursework deadlines

Course content

Part 1 compulsory modules

Part 2 compulsory modules

Part 2 optional modules

Students on the 9-month (12-month) programme can select 40 (20) credits from the following modules:

Part 3 optional modules

Optional modules

Students on the 12-months programme should take 20 credits from the following:

Learning options

Full-time: 9 months Full-time: 12 months

Students will be resident and undertake full-time study in the UK. Under both, the 9 and 12-month programmes students take compulsory and/or elective modules in Part 2.

The 12 month option involves taking an elective 20 credit module between July and August, which would also mean a 20 credit reduction in the number of taught modules taken in the spring term.

Careers

Many of our financial engineering graduates are now working as Quants in large London banks and other financial institutions. Others have pursued PhDs and have successful academic careers. Financial instruments are becoming ever more sophisticated, so graduates that understand complex modelling techniques are always in great demand. The high quantitative content of this programme opens many doors to a wide range of careers. You could structure and develop new debt or equity solutions to meet clients funding and hedging needs, or you could become a proprietary trader in exotic derivatives, or a software specialist or a quantitative analyst supporting the traders.

There are excellent opportunities on the buy-side, with hedge funds and investment institutions, as well as in investment banking and in software analytics. Opportunities in quantitative research, or with a rating agency, are among the many other attractive alternatives. Outside of mainstream banking and investment, you might also consider firms involved in commodity and energy trading, or the treasury divisions of leading multinationals and management consultancies.

Professional accreditation

ICMA Fixed Income Certificate

To obtain the requisite knowledge to pass the rigorous FIC exam, students are required to take the ICMA Centre Fixed Income Cash and Derivatives Markets module at Part 2. In order to receive the FIC certificate, students will need to register and pass the FIC exam through ICMA.



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The programme provides graduates with strong mathematical skills, the necessary computational techniques and finance background relevant to subsequent employment in a sector of finance such as investment banks, hedge funds, insurance companies and the finance departments of large corporations where mathematics plays a key role. Read more
The programme provides graduates with strong mathematical skills, the necessary computational techniques and finance background relevant to subsequent employment in a sector of finance such as investment banks, hedge funds, insurance companies and the finance departments of large corporations where mathematics plays a key role.

The depth of the mathematics taught should enable graduates to pursue research careers in stochastic analysis, financial mathematics or other relevant areas.

The period October to June is devoted to lectures, tutorials and practical sessions comprising the core and optional modules. This is followed by a period of about 14 weeks devoted to an individual project.

Core study areas include measure theory and martingales, stochastic models in finance, stochastic calculus and theory of stochastic pricing and a research project.

Optional study areas include programming and numerical methods, regular and chaotic dynamics, financial economics, functional analysis, elements of PDEs, static and dynamic optimisation, asset management and derivatives, and corporate finance

See the website http://www.lboro.ac.uk/study/postgraduate/programmes/departments/mathematics/mathematical-finance/

Programme modules

Semester 1:
Compulsory Modules
- Introduction to Measure Theory and Martingales
- Stochastic Models in Finance

Optional Modules (choose two)
- Programming and Numerical Methods
- Regular and Chaotic Dynamics
- Financial Economics

Semester 2:
Compulsory Modules
- Stochastic Calculus and Theory of Stochastic Pricing
- Research Project

Optional Modules (choose three)
- Functional Analysis
- Elements of PDEs
- Static and Dynamic Optimisation
- Either Asset Management and Derivatives or Corporate Finance

Assessment

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

Careers and further study

This programme may lead to a wide range of employment within industry, the financial sectors, and research establishments. It may also provide an ideal background for postgraduate research in Stochastic Analysis, Probability Theory, Mathematical Finance and other relevant areas.

Scholarships and sponsorships

A number of part-fee studentships may be available to appropriately qualified international students.

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/mathematical-finance/

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The development of new materials lies at the heart of many of the technological challenges we currently face, for example creating advanced materials for energy generation. Read more

Overview

The development of new materials lies at the heart of many of the technological challenges we currently face, for example creating advanced materials for energy generation. Computational modelling plays an increasingly important role in the understanding, development and optimisation of new materials. This four year Doctoral Training Programme on computational methods for material modelling aims to train scientists not only in the use of existing modelling methods but also in the underlying computational and mathematical techniques. This will allow students to develop and enhance existing methods, for instance by introducing new capabilities and functionalities, and also to create innovative new software tools for materials modelling in industrial and academic research. The first year of the CDT is a materials modelling option within the MPhil in Scientific Computing (please see the relevant entry) at the University of Cambridge and a range of additional training elements.

The MPhil in Scientific Computing is administered by the Department of Physics, but it serves the training needs of the Schools of Physical Sciences, Technology and Biological Sciences. The ability to have a single Master’s course for such a broad range of disciplines and applications is achieved by offering core (i.e. common for all students) numerical and High Performance Computing (HPC) lecture courses, and complementing them with elective courses relevant to the specific discipline applications.

In this way, it is possible to generate a bespoke training portfolio for each student without losing the benefits of a cohort training approach. This bespoke course is fully flexible in allowing each student to liaise with their academic or industrial supervisor to choose a study area of mutual interest.

See the website http://www.graduate.study.cam.ac.uk/courses/directory/pcphpdcms

Learning Outcomes

By the end of the course, students will have:
- a comprehensive understanding of numerical methods, and a thorough knowledge of the literature, applicable to their own research;
- demonstrated originality in the application of knowledge, together with a practical understanding of how research and enquiry are used to create and interpret knowledge in their field;
- shown abilities in the critical evaluation of current research and research techniques and methodologies;
- demonstrated self-direction and originality in tackling and solving problems, and acted autonomously in the planning and implementation of research.

Teaching

The first year of the CDT has a research as well as a taught element. The students attend lecture courses during the first five months (October-February) and then they will undertake a substantial Research Project over the next 6 months (from March to the end of August) in a participating Department. The research element aims to provide essential skills for a successful completion of the PhD, as well as to assess and enhance the research capacity of the students. It is based on a materials science topic which is studied by means of scientific computation. Research project topics will be provided by academic supervisors or by the industrial partners. Most of the projects are expected to make use the University’s High Performance Computing Service (for which CPU time for training and research has been budgeted for every student).

The taught element comprises core lecture courses on topics of all aspects of scientific computing, and elective lecture courses relevant to the topic of the research project. There is equal examination credit weighting between the taught and the research elements of the course, which is gained by submitting a dissertation on the project and by written assignments and examinations on the core and elective courses, respectively. Weighting of the assessed course components is as follows: Dissertation (research) 50%; written assignments 25%; written examinations 25%.

The core courses are on topics of high-performance scientific computing and advanced numerical methods and techniques; they are taught and examined during the first five months (October-February). Their purpose is to provide the students with essential background knowledge for completing their theses and for their general education in scientific computing.

Appropriate elective courses are selected from Master’s-level courses offered by the Departments of the School of Physical Sciences, Technology or Biological Sciences. The choice of courses will be such as to provide the students with essential background knowledge for completing their theses and for their general education in the materials science application of the project. They are decided in consultation with the project supervisor.

Depending on the materials science application of the research topic, students will follow one of the following two numerical methodology options: a) Continuum methods based on systems of partial differential equations (PDEs, e.g. finite-difference, element or volume methods); or b) atomistic approaches, which can be based on classical particle-based modelling (e.g. molecular dynamics) or on electronic structure- based methods (e.g. density functional theory). The students who take the atomistic modelling options will attend a 12-lecture course before continuing to classical particle-based methods or electronic structure methods. Irrespective of the numerical methodology option, students will attend lecture courses on High Performance Computing topics and elements of Numerical Analysis.

In addition to the comprehensive set of Masters-level courses provided by the MPhil and across the University in the field, which will be available to the CDT students, it will also be possible for students to take supplementary courses (not for examination) at undergraduate level, where a specific need is identified, in order to ensure that any prerequisite knowledge for the Masters courses is in place.

Moreover, depending on their background and circumstances, students may be offered places in the EPSRC-funded Autumn Academy, which takes place just before the start of the academic year (two weeks in September).

Funding Opportunities

Studentships funded by EPSRC and/or Industrial and other partners are available subject to eligibility criteria.

General Funding Opportunities http://www.graduate.study.cam.ac.uk/finance/funding

Find out how to apply here http://www.graduate.study.cam.ac.uk/courses/directory/pcphpdcms/apply

See the website http://www.graduate.study.cam.ac.uk/courses/directory/pcphpdcms

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This one-year programme at the University of Edinburgh will immerse you in the most current developments in chemical engineering, through a combination of taught modules, workshops, a research dissertation, and a number of supporting activities delivered by the key experts in the field. Read more

This one-year programme at the University of Edinburgh will immerse you in the most current developments in chemical engineering, through a combination of taught modules, workshops, a research dissertation, and a number of supporting activities delivered by the key experts in the field.

The programme will develop from fundamental topics, including modern approaches to understanding properties of the systems on a molecular scale and advanced numerical methods, to the actual processes, with a particular emphasis on energy efficiency, to the summer dissertation projects where the acquired skills in various areas are put into practice, in application to actual chemical engineering problems.

Programme structure

The programme develops from compulsory courses, emphasizing modern computational techniques and research methods, to a range of options. It is complemented by a strong management and economics component.

Compulsory Courses

  • Numerical Methods for Chemical Engineers
  • Molecular Thermodynamics
  • Introduction to Research Methods

In addition to the compulsory courses you will take a range of optional courses, please review the "Degree Structure" portion the MSc website listed below to find further information on available courses and course descriptions:

Learning outcomes

  • A working knowledge of modern modelling and simulation approaches to understanding properties of chemical systems at a molecular level.
  • A working knowledge of advanced experimental techniques, such as for example particle image velocimetry, spectroscopy and infra-red thermography, as applied in engineering research and development.
  • Ability to transform a chemical engineering problem into a mathematical representation; broad understanding of the available numerical tools and methods to solve the problem; appreciation of their scope and limitations.
  • An understanding of the basic design approaches to advanced energy efficient separation processes.
  • Ability to transfer and operate engineering principles in application to other fields, such as biology.
  • Proficiency in using modern chemical engineering software, from molecular visualisation to computational fluid dynamics to process engineering.

On completion of the research dissertation, the students will be able to:

  • Plan and execute a significant research project
  • Apply a range of standard and specialised research instruments and techniques of enquiry
  • Identify, conceptualise and define new and abstract problems and issues
  • Develop original and creative responses to problems and issues
  • Critically review, consolidate and extend knowledge, skills practices and thinking in chemical engineering
  • Communicate their research findings, using appropriate methods, to a range of audiences with different levels of knowledge and expertise
  • Place their research in the context of the current societal needs and industrial practice
  • Adhere to rigorous research ethics rules
  • Exercise substantial autonomy and initiative in research activities
  • Take responsibility for independent work
  • Communicate with the public, peers, more senior colleagues and specialists
  • Use a wide range of software to support and present research plans and findings

Career opportunities

Our graduates enjoy diverse career opportunities in oil and gas, pharmaceutical, food and drink, consumer products, banking and consulting industries. Examples of the recent employers of our graduates include BP, P&G, Mondelēz International, Doosan Babcock, Atkins, Safetec, Xodus Group, Diageo, Wood Group, GSK, Gilead Sciences, ExxonMobil, Jacobs, Halliburton, Cavendish Nuclear to name a few. This wide range of potential employers means that our graduates are exceptionally well placed to find rewarding and lucrative careers. According to the Complete University Guide, the chemical engineering programme at the University of Edinburgh is ranked one of the top in the UK in terms of graduates prospects.

Find our more about career opportunities:

The MSc in Advanced Chemical Engineering may also lead to further studies in a PhD programme. With the 94% of our research activity rated as world leading or internationally excellent (according to the most recent Research Excellence Framework 2014), Edinburgh is the UK powerhouse in Engineering. As an MSc student at Edinburgh you will be immersed in a research intensive, multidisciplinary environment and you will have plenty of opportunities to interact with PhD, MSc students and staff from other programmes, institutes and schools.

Find out more about our research:



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Applied Mathematics is concerned with mathematical methods used in industry, science, business and engineering. Studying this subject at doctoral level is an opportunity to become an specialist in the mathematics that powers business and society. Read more
Applied Mathematics is concerned with mathematical methods used in industry, science, business and engineering. Studying this subject at doctoral level is an opportunity to become an specialist in the mathematics that powers business and society.

As a researcher in the School of Mathematics and Physics, you have the opportunity to work with specialists in the field and may have the chance to develop national and international collaborations.

Research in the School follows two distinct strands: computational physics/applied mathematics and pure mathematics. Research in applied mathematics is focused on the development of numerical methods and algorithms for solving various equations found in materials science, specifically in the field of nanomaterials. This includes advanced parallelisation and adaptation of the methods for modern supercomputers.

Research Areas, Projects & Topics

Main Research Areas:
-Numerical Methods for non-linear Partial Differential Equations in Materials Science
-High Performance Computing in Materials Science

For information about the School’s research activity please visit: http://www.lincoln.ac.uk/home/smp/research/

How You Study

You can benefit from specialist computational facilities, training programmes to enhance your research skills and support from dedicated academic supervisors. You will be supported and encouraged to submit papers to international scientific journals, present your findings at conferences and share knowledge with colleagues across the University.

Due to the nature of postgraduate research programmes, the vast majority of your time will be spent in independent study and research. You will have meetings with your academic supervisors, however the regularity of these will vary depending on your own individual requirements, subject area, staff availability and the stage of your programme.

How You Are Assessed

A PhD is usually awarded based on the quality of your thesis and your ability in an oral examination (viva voce) to present and successfully defend your chosen research topic.

Career and Personal Development

Applied Mathematics students have the opportunity to develop the problem solving skills that may lead to careers in academia, research or industry. 

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