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Masters Degrees (Numerical)

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This course is for you if you wish to enter knowledge-led industrial sectors or to embark upon doctoral interdisciplinary study. This interdisciplinary programme between Mathematics, Engineering, Physics, and Astronomy gives you access to a broad range of knowledge and application in industry and academia. Read more

Why is this course for you?

•This course is for you if you wish to enter knowledge-led industrial sectors or to embark upon doctoral interdisciplinary study.
•This interdisciplinary programme between Mathematics, Engineering, Physics, and Astronomy gives you access to a broad range of knowledge and application in industry and academia.

What will you gain as a student?

•practical skills in computation in a range of languages and professional software
•rigorous understanding of the theory of common numerical methods
•technical knowledge in numerical modelling
•exposure to a range of common areas of application

Core Modules

Scientific Computing
Practical Programming
Computational Methods for PDEs or Finite Element Methods

Optional Modules include:

Topics in Mathematical Biology
Particle Methods in Scientific Computing
Advanced Fluid Dynamics
Data Mining and Neural Networks
Computational Fluid Dynamics
Dynamics of Mechanical Systems
Applications in Theoretical Physics

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The Applied Mathematics group in the School of Mathematics at the University of Manchester has a long-standing international reputation for its research. Read more

The Applied Mathematics group in the School of Mathematics at the University of Manchester has a long-standing international reputation for its research. Expertise in the group encompasses a broad range of topics, including Continuum Mechanics, Analysis & Dynamical Systems, Industrial & Applied Mathematics, Inverse Problems, Mathematical Finance, and Numerical Analysis & Scientific Computing. The group has a strongly interdisciplinary research ethos, which it pursues in areas such as Mathematics in the Life Sciences, Uncertainty Quantification & Data Science, and within the Manchester Centre for Nonlinear Dynamics.

The Applied Mathematics group offers the MSc in Applied Mathematics as an entry point to graduate study. The MSc has two pathways, reflecting the existing strengths within the group in numerical analysis and in industrial mathematics. The MSc consists of five core modules (total 75 credits) covering the main areas of mathematical techniques, modelling and computing skills necessary to become a modern applied mathematician. Students then choose three options, chosen from specific pathways in numerical analysis and industrial modelling (total 45 credits). Finally, a dissertation (60 credits) is undertaken with supervision from a member of staff in the applied mathematics group with the possibility of co-supervision with an industrial sponsor. 

Aims

The course aims to develop core skills in applied mathematics and allows students to specialise in industrial modelling or numerical analysis, in preparation for study towards a PhD or a career using mathematics within industry. An important element is the course regarding transferable skills which will link with academics and employers to deliver important skills for a successful transition to a research career or the industrial workplace.

Special features

The course features a transferable skills module, with guest lectures from industrial partners. Some dissertation projects and short internships will also be available with industry.

Teaching and learning

Students take eight taught modules and write a dissertation. The taught modules feature a variety of teaching methods, including lectures, coursework, and computing and modelling projects (both individually and in groups). The modules on Scientific Computing and Transferable Skills particularly involve significant project work. Modules are examined through both coursework and examinations.

Coursework and assessment

Assessment comprises course work, exams in January and May, followed by a dissertation carried out and written up between June and September. The dissertation counts for 60 credits of the 180 credits and is chosen from a range of available projects, including projects suggested by industrial partners.

Course unit details

Course unit details

 CORE (75 credits)

 * Introduction to Uncertainty Quantification

 * Mathematical Methods

 * Partial Differential Equations

 * Scientific Computing

 * Transferable Skills for Applied Mathematicians

 OPTIONAL (3 modules, 45 credits)

 * Applied Dynamical Systems (IM)

 * Continuum Mechanics (IM)

 * Stability theory (IM)

 * Transport Phenomena and Conservation Laws (IM)

 * Advanced Uncertainty Quantification (IM,NA)

 * Approximation Theory and Finite Element Analysis (NA)

 * Numerical Linear Algebra (NA)

 * Numerical Optimization and Inverse Problems (NA)

Students registered on the Numerical Analysis pathway must select modules marked NA, and those registered on the Industrial Modelling pathway must select modules marked IM.

Syllabuses for the modules Introduction to Uncertainty Quantification and Advanced Uncertainty Quantification are currently being finalized and details will be added here as soon as possible.

Facilities

Modern computing facilities are available to support the course.

Disability support

Practical support and advice for current students and applicants is available from the Disability Advisory and Support Service. Email: 

Career opportunities

The programme will prepare students for a career in research (via entry into a PhD programme) or direct entry into industry. Possible subsequent PhD programmes would be those in mathematics, computer science, or one of the many science and engineering disciplines where applied mathematics is crucial. The programme develops many computational, analytical, and modelling skills, which are valued by a wide range of employers. Specialist skills in scientific computing are valued in the science, engineering, and financial sector.



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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.

About this degree

This programme aims to provide a rigorous formal training in computational science 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 Computing

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.

Further information on modules and degree structure is available on the department website: Scientific Computing MSc

Funding

For a comprehensive list of the funding opportunities available at UCL, including funding relevant to your nationality, please visit the Scholarships and Funding website.

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 enables 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 7th in QS World University Rankings 2018). Furthermore, the Thomson Scientific Citation Index shows that UCL is the second-most highly cited European university and 12th 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 enables students to tackle real-life problems in a structured and rigorous way and produce professional software for their efficient solution.



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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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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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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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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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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 MSc in Computational Mathematical Finance (CMF) is a dynamic new programme with the aim to deliver high quality training in the theory of Mathematical Finance with strong emphasis on computational methods. Read more

The MSc in Computational Mathematical Finance (CMF) is a dynamic new programme with the aim to deliver high quality training in the theory of Mathematical Finance with strong emphasis on computational methods.

Currently graduates in this field are expected to have a working knowledge of advanced computational finance (including construction of algorithms and programming skills) as well as a sound knowledge of the theory of Probability and Stochastic Analysis. These are the core theories needed in the modern valuation of complex financial instruments.

This MSc programme delivers:

  • a flexible programme of study relevant to the needs of employers such as: top investment banks, hedge funds and asset management firms
  • a solid knowledge in financial derivative pricing, risk management and portfolio management
  • the transferable computational skills required by the modern quantitative finance world

Programme structure

You must obtain a total of 180 credits to be awarded the MSc. Over semesters 1 and 2, you will take compulsory courses worth a total of 85 credits and optional courses worth a further 35 credits. Successful performance in these courses (assessed through coursework or examinations or both) allows you to start work on a three-month dissertation project, worth 60 credits, for the award of the MSc degree.

There are two streams: the Financial stream and the Computational stream.

Compulsory courses previously offered include (both streams):

  • Stochastic Analysis in Finance (20 credits, semester 1)
  • Discrete-Time Finance (10 credits, semester 1)
  • Finance, Risk and Uncertainty (10 credits, semester 1)
  • Object-Oriented Programming with Applications (10 credits, semester 1)
  • Risk-Neutral Asset Pricing (10 credits, semester 2)
  • Stochastic Control and Dynamic Asset allocation (10 credits, semester 2)
  • Monte Carlo Methods (5 credits, semester 2)
  • Numerical Methods for Stochastic Differential Equations (5 credits, semester 2)
  • Research-Linked Topics (10 credits, semesters 1 and 2)

Additional compulsory courses for Computational Stream previously offered include:

  • Numerical Partial Differential Equations (10 credits, semester 2)
  • Time Series (10 credits, semester 2)

Additional compulsory courses for Financial stream previously offered include:

  • Financial Risk Theory (10 credits, semester 2)
  • Optimization Methods in Finance (10 credits, semester 2)

Optional courses previously offered include:

  • Numerical Partial Differential Equations (10 credits, semester 2)
  • Time Series (10 credits, semester 2)
  • Financial Risk Theory (10 credits, semester 2)
  • Optimization Methods in Finance (10 credits, semester 2)
  • Integer and Combinatorial Optimization (10 credits, semester 2)
  • Bayesian Theory (10 credits, semester 1)
  • Credit Scoring (10 credits, semester 2)
  • Python Programming (10 credits, semester 1)
  • Scientific Computing (10 credits, semester 1)
  • Programming Skills - HPC MSc (10 credits, semester 1)
  • Parallel Numerical Algorithms - HPC MSc (10 credits, semester 1)
  • Applied Databases (10 credits)

Work placements/internships

We work closely with the Scottish Financial Risk Academy (SFRA) to offer a number of short courses led by industry (part of our Research-Linked Topics) and to provide the opportunity to our best students to write their dissertations during placements with financial services companies.

Learning outcomes

At the end of this programme you will have:

  • developed personal communications skills, initiative, and professionalism within a mathematical context
  • developed transferable skills that maximise your prospects for future employment, including writing, oral presentation, team-working, numerical and logical problem-solving, planning and time-management
  • improved your ability to convey ideas in an articulate fashion, to build upon previous mathematical training and further develop logic and deductive skills
  • mastered standard and advanced mathematical tools used to solve applied problems relevant to the mathematical finance industry
  • developed quantitative and computational skills for the proficient fulfilment of tasks in the financial sector

Career opportunities

Graduates can expect to go on to work in major financial institutions or to continue their studies by joining PhD programmes.



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This programme gives you a flexible syllabus to suit the demands of employers that use modern financial tools and optimization techniques in areas such as the financial sector and energy markets. Read more

This programme gives you a flexible syllabus to suit the demands of employers that use modern financial tools and optimization techniques in areas such as the financial sector and energy markets.

We will give you sound knowledge in financial derivative pricing, portfolio optimization and financial risk management.

We will also provide you with the skills to solve some of today’s financial problems, which have themselves been caused by modern financial instruments. This expertise includes modern probability theory, applied statistics, stochastic analysis and optimization.

Adding depth to your learning, our work placement programme puts you at the heart of financial organisations such as Moody's Analytics, Standard Life Investment and Lloyds Banking Group.

Programme structure

This programme involves two taught semesters of compulsory and option courses, followed by a dissertation project. You must obtain a total of 180 credits to be awarded the MSc. Over semesters 1 and 2, you will take a number of compulsory courses and optional courses. Successful performance in these courses (assessed through coursework or examinations or both) allows you to start work on a three-month dissertation project worth 60 credits, possibly with one of our industry partners, for the award of the MSc degree.

Compulsory courses:

  • Discrete-Time Finance (10 credits, S1)
  • Stochastic Analysis in Finance (20 credits, S1)
  • Fundamentals of Optimization (10 credits, S1)
  • Research-Linked Topics (10 credits, full-year)
  • Finance, Risk and Uncertainty (10 credits, S1)
  • Risk-Neutral Asset Pricing (10 credits, S2)
  • Simulation (10 points, S2)
  • Optimization Methods in Finance (10 credits, S2)

Optional courses:

  1. Operations Research and Mathematical Finance courses:
  • Financial Risk Theory (10 credits, S1)
  • Computing for Operational Research and Finance (10 credits, S1)
  • Fundamentals of Operational Research (10 credits, S1)
  • Stochastic Control and Dynamic Asset Allocation (10 credits, S2)
  • Credit Scoring (10 credits, S2)
  • Financial Risk Management (10 credits, S2)
  • Risk Analysis (5 credits, S2)
  • Stochastic Modelling (10 credits, S2)
  1. Relevant Statistical and Numerical courses:
  • Multivariate Data Analysis (10 credits, S2)
  • Numerical Partial Differential Equations (10 credits, S2)
  • Advanced Time Series Econometrics (10 credits, S2) (offered by the School of Economics)
  1. Programming courses:
  • Object-Oriented programming with applications (10 credits, S1)
  • Parallel Numerical Algorithms (10 credits, S1), (offered by EPCC)
  • Programming Skills (10 credits, S1), (offered by EPCC)
  1. Optimization courses:
  • Combinatorial Optimization (5 credits, S2)
  • Large Scale Optimization for Data Science (10 credits, S2)
  • Modern Optimization Methods for Big Data Problems (10 credits, S2)
  • Nonlinear Optimization (10 credits, S2)
  • Stochastic Optimization (5 credits, S2)

Work placements/internships

We work closely with the Scottish Financial Risk Academy (SFRA) to offer a number of short courses led by industry (part of our Research-Linked Topics) and to provide the opportunity to our best students to write their dissertations during placements with financial services companies.

Learning outcomes

At the end of this programme you will have:

  • developed personal communications skills, initiative, and professionalism within a mathematical context
  • developed transferable skills that maximise your prospects for future employment, including writing, oral presentation, team-working, numerical and logical problem-solving, planning and time-management
  • improved your ability to convey ideas in an articulate fashion, to build upon previous mathematical training and further develop logic and deductive skills
  • mastered standard and advanced mathematical tools used to solve applied problems relevant to the mathematical finance industry
  • developed quantitative and computational skills for the proficient fulfilment of tasks in the financial sector

Career opportunities

Graduates have gone on to work in major financial institutions or to continue their studies by joining PhD programmes.



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There are no other courses that provide dedicated specialist training in design and analysis of advanced lightweight structures in aerospace, automotive, marine and renewable energy industries. Read more

There are no other courses that provide dedicated specialist training in design and analysis of advanced lightweight structures in aerospace, automotive, marine and renewable energy industries. This is with respect to structural integrity and health monitoring over service life, which can be tailored to your career aspirations.

Delivered with a unique focus on industry challenges and concerns, this course will equip you with strong experimental, numerical and analytical skills in structural mechanics for both composite and metallic components. This will help you to practically apply this knowledge to solve real engineering problems.

Who is it for?

Students who enrol come from a variety of different backgrounds. Many have specific careers in mind, such as working in automotive or aerospace disciplines (structural design or crash protection), materials development for defence applications, or to work in the field of numerical code developments/consultancy.

Why this course?

Designing advanced structures through novel, lightweight materials is one of the key enabling technologies for both aerospace and automotive sectors to align with national targets for reduction of carbon. In reducing inherent structural weight, it is essential not to compromise safety, as structural integrity and designing for crashworthiness become key design drivers.

Understanding how aluminium or composite structures and materials perform over their life cycles under static and dynamic loading, including crash and bird strike, requires expertise in a range of areas. As new simulation and material technologies emerge, there is a continuing need for talented employees with a strong, applied understanding in structural analysis, together with competent technical skills in numerical simulation.

Informed by Industry

Established in 2003, this course is supported by close ties with industry, through student projects, specialist lectures and more importantly, by employing our graduates.

The MSc in Advanced Lightweight Structures and Impact is directed by an Industrial Advisory Panel comprising senior engineers from aerospace sectors. This maintains course relevancy and ensures that graduates are equipped with the skills and knowledge required by leading employers.

The Industry Advisory Panel includes representatives from:

  • Airbus
  • Rolls-Royce
  • Jaguar

Accreditation

The MSc in Advanced Lightweight Structures and Impact is accredited by Mechanical Engineers (IMechE) & Royal Aeronautical Society (RAes) on behalf of the Engineering Council as meeting the requirements for Further Learning for registration as a Chartered Engineer. Candidates must hold a CEng accredited BEng/BSc (Hons) undergraduate first degree to comply with full CEng registration requirements.

Course details

You will complete eight compulsory modules.

The course employs a wide range of teaching methods designed to create a demanding and varied learning environment including structured lecture programmes, tutorials, case studies, hands-on computing, individual projects, and guest lectures.

Group project

The group project aims to address one of the greatest challenges graduates face, which is the lack of experience in dealing with the complexities of working within a design team. This part of the course takes place from March to May. It is student-led and consolidates the taught material which develops both technical and project management skills on an industrially relevant project.

On successful completion of this module a student should be able to:

  • Set objectives, plan and manage projects
  • Evaluate a project brief set by a client
  • Develop a set of project objectives appropriate to the client’s brief
  • Plan and execute a work programme with reference to key project management processes (e.g. time management; risk management; contingency planning; resource allocation).

The projects are designed to integrate knowledge, understanding and skills from the taught modules in a real-life situation. This module is typically delivered through collaboration with an industrial sponsor.

Individual project

Individual research project topics can vary greatly, allowing you to develop your own areas of interest. It is common for our industrial partners to put forward real-life practical problems or areas of development as potential research topics. This section of the course takes place from April to August.

The research projects are devised to provide a research challenge allowing you to; define the problem, perform appropriate analysis and research, draw conclusions from your work, communicate your findings and conclusions and enhance your skills and expertise. This will enable you to plan a research project, demonstrate a thorough understanding of your chosen topic area, including a critical evaluation of existing work, design appropriate analysis, plan an independent learning ability and manage a well-argued thesis report demonstrating original thought.

Cranfield University is a member of the European SOCRATES Mobility Programme and students may apply to undertake their Individual Research Project at other member institutions within Europe.

Assessment

Taught modules 40%, Group project 20%, Individual research project 40%

Your career

Industry driven research makes our graduates some of the most desirable in the world for recruitment by companies competing in the structural engineering sector, which forms a large worldwide industry.

Students who enrol come from a variety of different backgrounds. Many have specific careers in mind, such as working in automotive or aerospace disciplines (structural design, or crash protection), materials development for defence applications, or to work in the field of numerical code developments/consultancy. Others decide to continue their education through PhD studies available within the University.

This course provides graduates with the necessary skills to pursue a successful career in automotive, aerospace, maritime and defence sectors. This approach offers you a wide range of career choices as a structural engineer at graduation and in the future.

Companies that have recruited graduates of this course include:

  • Airbus
  • Rolls-Royce
  • Jaguar Land Rover
  • Aston Martin.


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Our MSc in Applied Computational Science and Engineering will educate future domain-specialists in computational science. This course will expand your knowledge of numerical methods, computational science, and how to solve large scale problems by applying novel science and engineering approaches. Read more

Our MSc in Applied Computational Science and Engineering will educate future domain-specialists in computational science.

This course will expand your knowledge of numerical methods, computational science, and how to solve large scale problems by applying novel science and engineering approaches. It is suitable for graduates of disciplines including maths and physical sciences, geophysics and engineering, and computer science.

This immersive, hands-on MSc course will enable students to develop their skills and techniques for a range of science and engineering applications utilising High Performance Computing resources. Students will learn alongside world-class researchers in the Department of Earth Science and Engineering.

There will be a strong emphasis on high productivity problem solving using modern computational methods and technologies, including computer code development and parallel algorithms.

Applicants who want to pursue analytical careers in industry geoscience and engineering are a target for this course. Graduates will develop the skills necessary to enter the modern industrial workforce.

This MSc will also prepare for your PhD studies in fields such as computational techniques, numerical analysis, optimisation and inversion, fluid mechanics, heat transfer, and machine learning applications.

The Applied Computational Science and Engineering MSc programme will ensure that students are able to apply appropriate computational techniques to understand, define and develop solutions to a range of science and engineering problems.

Students will have the chance to participate in individual and group research projects as well as to write reports and present technical work, developing the project management and numerical skills that employers want.

Careers

Graduates of this course will fill the market demand for those with applied, hands-on computational experience who can solve real world problems.

Through the combination of programming, foundational domain knowledge and advanced numerical literacy that this course provides, graduates will be highly sought after to work as expert analysts in industry, for example oil and gas, mineral exploration and climate science.

Graduates will be in an ideal position to pursue academic careers in fields such as computational techniques, optimisation and inversion, fluid mechanics, and machine learning applications.

Further information

For full information on this course, including how to apply, see: http://www.imperial.ac.uk/study/pg/earth-science/computational-science/

If you have any enquiries you can contact our team at:



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This course focuses on the physical processes that generate natural hazards through an advanced understanding of geological and environmental processes. Read more

Why take this course?

This course focuses on the physical processes that generate natural hazards through an advanced understanding of geological and environmental processes.

You will be fully trained by internationally recognised experts in hazard identification, terrain evaluation techniques as well as hazard modelling and risk assessment techniques. Providing you with the essential skills to monitor, warn and help control the consequences of natural hazards.

What opportunities might it lead to?

This course is accredited by the Geological Society of London. It offers advanced professional and scientific training providing an accelerated route for you to attain Chartered Status, such as Chartered Geologist (CGeol) and Chartered Scientist (CSci) on graduation.

Here are some routes our graduates have pursued:

Aid organisations
Environmental organisations
Offshore work
Civil sector roles
Mining
Insurance companies

Module Details

You can opt to take this course in full-time or part-time mode.

The course is divided into two parts. The first part comprises the lecture, workshop, practical and field work elements of the course, followed by a five-month independent research project. The course is a mixture of taught units and research project covering topics including site investigation, hazard modelling and mapping, soil mechanics and rock mechanics, contaminated land, flooding and slope stability.

Here are the units you will study:

Natural Hazard Processes: The topic of this unit forms the backbone of the course and give you an advanced knowledge of a broad range of geological and environmental hazards, including floods, landslides, collapsible ground, volcanoes, earthquakes, tsunamis, hydro-meteorological and anthropogenic hazards. External speakers are used to provide insights and expertise from an industry, regulatory and research perspective.

Numerical Hazard Modelling and Simulation: This forms an important part of the course, whereby you are trained in the application of computer models to the simulation of a range of geological and environmental hazards. You will develop skills in computer programming languages and use them to develop numerical models that are then used to simulate different natural hazard scenarios.

Catastrophe Modelling: On this unit you will cover the application of natural hazard modelling to better understand the insurance sector exposure to a range of geological and environmental hazards. It includes external speakers and sessions on the application of models for this type of catastrophe modelling.

Volcanology and Seismology: You will gain an in-depth knowledge of the nature of volcanism and associated hazards and seismology, associated seismo-tectonics and earthquake hazards. This unit is underpinned by a residential field course in the Mediterranean region that examines the field expression of volcanic, seismic and other natural hazards.

Flooding and Hydrological Hazards: These are a significant global problem that affect urban environments, one that is likely to increase with climate change. This unit will give you an in-depth background to these hazards and opportunities to simulate flooding in order to model the flood hazard and calculate the risk.

Hazard and Risk Assessment: This unit gives you the chance to study the techniques that are employed once a hazard has been identified and its likely impact needs to be measured. You will have advanced training in the application of qualitative and quantitative approaches to hazard and risk assessment and their use in the study of different natural hazards.

Field Reconnaissance and Geomorphological Mapping: These techniques are integral to the course and an essential skill for any graduate wishing to work in this area of natural hazard assessment. On this unit you will have fieldwork training in hazard recognition using techniques such as geomorphological mapping and walk-over surveys, combined with interpretation of remote sensing and aerial photography imagery.

Spatial Analysis and Remote Sensing: You will learn how to acquire and interpret aerial photography and satellite imagery, and the integration and analysis of spatial datasets using GIS – all key tools for hazard specialists.

Geo-mechanical Behaviour of Earth Materials: You will train in geotechnical testing and description of soils and rocks to the British and international standards used by industry.

Landslides and Slope Instability: This unit will give you an advanced understanding of landslide systems, types of slides in soils and rocks and methods for identification and numerical analysis.

Impacts and Remediation of Natural Hazards: You will cover a growing area of study, including the impact of hazardous events on society and the environment, and potential mitigation and remediation methods that can be employed.

Independent Research Project: This provides you with an opportunity to undertake an original piece of research to academic or industrial standards, typically in collaboration with research staff in the department or external industry partners. In addition to submission of a thesis report, you also present the results of your project at the annual postgraduate conference held at the end of September.

Programme Assessment

The course provides a balanced structure of lectures, seminars, tutorials and workshops. You will learn through hands-on practical sessions designed to give you the skills in laboratory, computer and field techniques. The course also includes extensive field work designed to provide field mapping and data collection skills.

Assessment is varied, aimed at developing skills relevant to a range of working environments. Here’s how we assess your work:

Poster and oral presentations
Project reports
Literature reviews
Lab reports
Essays

Student Destinations

This course provides vocational skills designed to enable you to enter this specialist environmental field. These skills include field mapping, report writing, meeting deadlines, team working, presentation skills, advanced data modelling and communication.

You will be fully equipped to gain employment in the insurance industry, government agencies and specialist geoscience companies, all of which are tasked with identifying and dealing with natural hazards. Previous destinations of our graduates have included major re-insurance companies, geological and geotechnical consultancies, local government and government agencies.

It also has strong research and analytical components, ideal if you wish to pursue further research to PhD level.

We aim to provide you with as much support as possible in finding employment through close industrial contacts, careers events, recruitment fairs and individual advice.

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This course is one of the premier international applied petroleum geoscience courses. Since the inception of the course in 1985 its graduates have an unparalleled employment record in the petroleum industry both in the UK and worldwide. Read more

This course is one of the premier international applied petroleum geoscience courses. Since the inception of the course in 1985 its graduates have an unparalleled employment record in the petroleum industry both in the UK and worldwide. In addition our graduates are highly sought after for further PhD research in the petroleum geosciences.

● Recognised by NERC - 5 MSc studentships each year covering fees, fieldwork and maintenance.

● Recognised by Industry - Industry scholarships

● We offer highly focused teaching and training by internationally recognised academic experts as well as by visiting staff from the petroleum industry.

The course covers the applications of basin dynamics and evolution to hydrocarbon exploration and production. The course is modular in form providing intensive learning and training in geophysics, tectonics and structural geology, sequence stratigraphy and sedimentology, hydrocarbon systems, reservoir geology, remote sensing and applied geological fieldwork.

The MSc course provides ‘state of the art’ training in -

● 3D seismic interpretation and 3D visualization;

● Fault analysis and fault-sealing;

● Seismic sequence stratigraphy;

● Applied sedimentology;

● Well log analysis;

● Remote sensing analysis of satellite and radar imagery;

● Analysis of gravity and magnetic data;

● Numerical modelling of sedimentation and tectonics;

● Applied structural geology;

● Geological Fieldwork.

● Transferable skills learned during the course include

project planning, presentation techniques, report writing and compilation, team working skills, spreadsheet and statistical analyses, GIS methods as well as graphics and visualization techniques.

● The full time MSc course runs for 50 weeks. The first half comprises one and two week course modules as well as group projects and fieldwork. The second half of the MSc course consists of an individual research project usually carried out in conjunction with the petroleum industry or related institutions such as international geological surveys.

● Part time study over 24 months is also available

● Each year independent projects are arranged with new data sets from industry – some students work in the offices of the company whereas other may use our excellent in-house facilities. All independent projects are supervised by faculty members with additional industry supervision where appropriate.

Facilities include –

● Dedicated Modern Teaching Laboratories

● 14 Dual Screen Unix Seismic Workstations

● PC and Macintosh Workstations

● Internationally Recognised Structural Modelling Laboratories

● Advanced Sedimentological Laboratories

The MSc course also greatly benefits from dynamic interaction with internationally recognised research groups within the Geology Department including –

● Project EAGLE – Evolution of the African and Arabian rift system – Professor Cindy Ebinger

● Southeast Asia Research Group – Tectonic Evolution and Basin Development in SE Asia – Professor Robert Hall

● Numerical Modelling Research Group – Numerical Modelling of Tectonics and Sedimentation – Dr Dave Waltham

● Fault Dynamics Research Group – Dynamics of Fault Systems in Sedimentary Basins – Professor Ken McClay

The 2005 MSc graduates went on to employment with Shell, BP, Amerada Hess, Gaz de France, OMV (Austria), Star Energy, First Africa Oil, Badley Ashton, ECL, PGS, Robertsons, PGL, Aceca, and to PhD research at Royal Holloway and Barcelona.

Since 2001, 85% of our graduates have gone in to work in the oil industry, 10% into geological research and 5% into environmental/engineering jobs.

Accommodation is available on campus in en-suite study bedrooms grouped in flats of eight, each with a communal kitchen and dining space.

Subsistence Costs ~£9,000 pa (including Hall of Residence fees of c. £4,500 for a full year)

APPLICATIONS can be made on line at http://www.rhul.ac.uk/Registry/Admissions/applyonline.html



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This is one of the premier international applied MSc courses with a focus on petroleum exploration and production. It is run in parallel with the Basin Evolution and Dynamics MSc in Petroleum Geocsience but with a greater emphasis on tectonics and structural geology. Read more
This is one of the premier international applied MSc courses with a focus on petroleum exploration and production. It is run in parallel with the Basin Evolution and Dynamics MSc in Petroleum Geocsience but with a greater emphasis on tectonics and structural geology. In addition to successful employment in the international petroleum industry graduates from this course are employed in the international mining industry as well as being highly sought after for further PhD research in the geosciences.

● Recognised by Industry - Industry scholarships

● We offer highly focused teaching and training by internationally recognised academic experts as well as by visiting staff from the petroleum and remote sensing industries.

The course covers the applications of tectonics and structural geology to hydrocarbon exploration and production as well as to applied structural geology research in different terranes. The course is modular in form providing intensive learning and training in tectonics, applied structural geology, seismic interpretation of structural styles, tectonostratigraphic analysis, section balancing and reconstruction, remote sensing, crustal fluids and hydrocarbon systems, reservoir geology, and applied geological fieldwork.

The MSc course provides ‘state of the art’ training in –
● Plate tectonics and terrane analysis;
● Applied structural analysis;
● 3D seismic interpretation and 3D visualization of structural styles;
● Fault analysis and fault-sealing;
● Tectonostratigraphic analysis;
● Scaled analogue modelling;
● Numerical modelling of structures;
● Remote sensing analysis of satellite and radar imagery;
● Analysis of gravity and magnetic data;
● Section balancing and reconstruction;
● Applied structural fieldwork.

● Transferable skills learned during the course include
project planning, presentation techniques, report writing and compilation, team working skills, spreadsheet and statistical analyses, GIS methods as well as graphics and visualization techniques.

● The full time MSc course runs for 50 weeks. The first half comprises one and two week course modules as well as group projects and fieldwork. The second half of the MSc course consists of an individual research project usually carried out in conjunction with the petroleum industry or related institutions such as international geological surveys.

● Part time study over 24 months is also available

● Each year independent projects are arranged with new data sets from industry – some students work in the offices of the company whereas other may use our excellent in-house facilities. All independent projects are supervised by faculty members with additional industry supervision where appropriate.

Facilities include –
● Dedicated Modern Teaching Laboratories
● Internationally Recognised Structural Modelling Laboratories
● 14 Dual Screen Unix Seismic Workstations
● PC and Macintosh Workstations
● Advanced Sedimentological Laboratories

The MSc course also greatly benefits from dynamic interaction with internationally recognised research groups within the Geology Department including –

● Project EAGLE – Evolution of the African and Arabian rift system – Professor Cindy Ebinger
● Southeast Asia Research Group – tectonic evolution and basin development in SE Asia – Professor Robert Hall
● Numerical Modelling Research Group – Numerical modelling of tectonics and sedimentation – Dr Dave Waltham
● Fault Dynamics Research Group – Dynamics of Fault Systems in Sedimentary Basins – Professor Ken McClay

Our Tectonics MSc graduates have gained employment with Shell, BP, ECL, PGS, Sipetrol, PGL, Codelco, and to PhD research in a range of universities including Trieste, Barcelona, and Ulster universities.
Since 2001, 85% of our Petroleum Geosciences MSc graduates have gone in to work in the oil industry, 10% into geological research and 5% into environmental/engineering jobs.

Accommodation is available on campus in en-suite study bedrooms grouped in flats of eight, each with a communal kitchen and dining space.

Subsistence Costs ~£9,000 pa (including Hall of Residence fees of c. £4,500 for a full year)

APPLICATIONS can be made on line at http://www.rhul.ac.uk/Registry/Admissions/applyonline.html

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