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Masters Degrees (High Performance Computing)

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The Department of Computer Science at The University of Liverpool is delighted to announce the opportunity for Home and European students to receive industrial sponsorship to cover tuition fees for this programme. Read more
The Department of Computer Science at The University of Liverpool is delighted to announce the opportunity for Home and European students to receive industrial sponsorship to cover tuition fees for this programme. For more information visit our Postgraduate Funding Tool or contact Dr Martin Gairing.

The MSc in Big Data and High Performance Computing provides students with an in-depth understanding of big data analysis and processing using high performance computing technology. Run in conjuction with the STFC Hartree Centre, this MSc programme enables students to gain a specialist qualification in an area of computing that is in great demand worldwide.

Big data is commonly described as data that is so large that it cannot be readily processed using standard techniques. Our current global ability to collect data is such that “big data” sets are becoming common-place.

The most obvious example of this is the exponential growth of the World Wide Web; however there are many public and private enterprises where the analysis of large-scale data sets is critical to growth. Although significant computer power exists, the necessary skills-base is lagging behind the technology.

There is an employment gap looming in the field of big data, especially in the context of the skills required with respect to the application of High Performance Computing (HPC) capabilities to address big data problems.

The MSc in Big Data and High Performance Computing is designed to address this anticipated skills gap and provide those completing the programme with the necessary abilities (abilities which will be highly desirable within the employment market) to address big data centric problems in the context of HPC.

The programme has been designed and operates in close collaboration with the Hartree High Performance Computing Centre and focuses on the practical application of Big Data and HPC technology.

The Hartree centre is underpinned by £37.5 million of Government investment and hosts the UK’s premier supercomputing environment. This partnership provides a unique and unrivalled MSc programme and ensures that students completing the programme have a ready route into employment, facilitated by commercial contacts provided as part of the individual project.

You may also be interested in our Big Data Management MSc, Geographic Data Science MSc and Risk and Uncertainty MSc. For more information visit http://www.liverpool.ac.uk/study/postgraduate

The programme is organised as two taught semesters followed by an individual project undertaken over either the summer or, if desired, during the following year of study. Within each semester students study a number of modules adding up to 60 credits per semester (120 in total). This will be followed by a project dissertation, also 60 credits, making an overall total of 180 credits.

Why Computer Science?

Excellent partnerships

The MSc in Big Data and High Performance Computing programme has been developed, and operates, in close collaboration with the STFC Hartree Centre at Daresbury. The Hartree centre is underpinned by £37.5 million off Government investment and hosts the UKs premier supercomputing environment. The Department of Computer Science at Liverpool provides for a wide range of Big data, HPC and related skills and experience. This partnership means that this programme is unique and unrivalled. The partnership also ensures that students completing the programme have a ready route into employment facilitated by commercial contacts provided as part of the individual project element of the programme, which will in most cases is conducted with respect to real commercial requirements.

State of the art teaching and research

MSc Students who pursue their postgraduate study within the Department of Computer Science at the University of Liverpool will be an integral part of a department that is internationally renowned for its advanced research and teaching. The Department came seventh nationally in the 2008 research assessment exercise.

The Department of Computer Science is organised into four main research groups:

Agents
Algorithmics
Logic and Computation
Economics and Computation
Together these groups provide a critical mass of expertise equal to the most complex challenges in Computer Science, within a setting that offers world-class research facilities and support.

Teaching

You will be taught by lecturers who are internationally known for their research. The MSc in Big Data and High Performance Computing is offered full-time on-campus.

The taught components of the programme offer a choice of contemporary computing topics, a strong theoretical basis and the opportunity to gain sound practical and critical analysis skills. The programme can be taken in the form of a single year (12 months) of study with the individual project being undertaken over the summer months, or alternatively the project can be undertaken in the following academic year.

The computing resources include an extensive integrated network of workstations running the Linux operating system and the X-Windows graphical interface, together with a large number of PCs running Microsoft Windows. Staff and students have easy access to high quality laser printing facilities and a range of specialist software.

Career prospects

The MSc in Big Data and High Performance Computing (HPC) is specifically designed to fill a "skills gap" in the employment market. More specifically it is designed to provide students with the necessary skills to allow them to apply Big Data and HPC concepts to real problems. The programme has been structured to facilitate the practical application of this "cutting-edge" technology to real-world problems. The intention is that at the end of the programme students will be able to apply the knowledge gained on the programme specifically to real-world big data and HPC problems. However, the programme is also designed to furnish students with a set of transferable skills that are of particular relevance across the IT industry.

The programme has been developed, and is delivered, in close collaboration with the Hartree Centre at Daresbury which operates the UK's largest supercomputer (capable of a thousand trillion calculations per second). Hartree have close links with industry, and provide assistance with respect to the group and final individual projects, the latter conducted in partnership with commercial and/or non-commercial organisations.

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You will study at EPCC, the UK’s leading supercomputing centre. EPCC is the major provider of high performance computing (HPC) training in Europe with an international reputation for excellence in HPC education and research. Read more

Programme description

You will study at EPCC, the UK’s leading supercomputing centre. EPCC is the major provider of high performance computing (HPC) training in Europe with an international reputation for excellence in HPC education and research.

Our staff have a wealth of expertise across all areas of HPC, parallel programming technologies and data science.

This MSc programme has a strong practical focus and provide access to leading- edge HPC systems such as ARCHER, which is the UK’s largest, fastest and most powerful supercomputer, with more than 100,000 CPU cores.

HPC is the use of powerful processors, networks and parallel supercomputers to tackle problems that are very computationally or data-intensive. You will learn leading-edge HPC technologies and skills to exploit the full potential of the world’s largest supercomputers and multicore processors. This is a well-established programme that has been successful in training generations of specialists in parallel programming.

Programme structure

The MSc programme takes the form of two semesters of taught courses followed by a dissertation project.

Your studies will have a strong practical focus and you will have access to a wide range of HPC platforms and technologies. You will take seven compulsory courses, which provide a broad-based coverage of the fundamentals of HPC, parallel computing and data science. The option courses focus on specialist areas relevant to computational science. Assessment is by a combination of coursework and examination.

Taught courses

Compulsory courses:

HPC Architectures (Semester 1)
Message-Passing Programming (Semester 1)
Programming Skills (Semester 1)
Threaded Programming (Semester 1)
Software Development (Semester 2)
Project Preparation (Semester 2)
HPC Ecosystem (Semester 2)

Optional courses:

Fundamentals of Data Management (Semester 1)
Parallel Numerical Algorithms (Semester 1)
Parallel Programming Languages (Semester 1)
Advanced Parallel Programming (Semester 2)
Data Analytics with High Performance Computing (Semester 2)
Parallel Design Patterns (Semester 2)
Performance Programming (Semester 2)
Courses from the School of Informatics, Mathematics or Physics (up to 30 credits)

Dissertation

After completing the taught courses, students work on a three-month individual project leading to a dissertation.

Dissertation projects may be either research-based or industry-based with an external organisation, with opportunities for placements in local companies.

Industry-based dissertation projects

Through our strong links with industry, we offer our students the opportunity to undertake their dissertation project with one of a wide range of local companies.

An industry-based dissertation project can give you the opportunity to enhance your skills and employability by tackling a real-world project, gaining workplace experience, exploring potential career paths and building relationships with local companies.

Career opportunities

Our graduates are employed across a range of commercial areas, for example software development, petroleum engineering, finance and HPC support. Others have gone on to PhD research in fields that use HPC technologies, including astrophysics, biology, chemistry, geosciences, informatics and materials science.

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You will study at EPCC, the UK’s leading supercomputing centre. EPCC is the major provider of high performance computing (HPC) training in Europe with an international reputation for excellence in HPC education and research. Read more

Programme description

You will study at EPCC, the UK’s leading supercomputing centre. EPCC is the major provider of high performance computing (HPC) training in Europe with an international reputation for excellence in HPC education and research.

Our staff have a wealth of expertise across all areas of HPC, parallel programming technologies and data science.

This MSc programme has a strong practical focus and provide access to leading- edge HPC systems such as ARCHER, which is the UK’s largest, fastest and most powerful supercomputer, with more than 100,000 CPU cores.

Data science involves the manipulation, processing and analysis of data to extract knowledge, and HPC provides the power that underpins it.

You will learn the multidisciplinary skills and knowledge in both HPC and data science to unlock the knowledge contained in the increasingly large, complex and challenging data sets that are now generated across many areas of science and business.

Programme structure

This MSc programme takes the form of two semesters of taught courses followed by a dissertation project.

Your studies will have a strong practical focus and you will have access to a wide range of HPC platforms and technologies. You will take seven compulsory courses, which provide a broad-based coverage of the fundamentals of HPC, parallel computing and data science. The option courses focus on specialist areas relevant to computational science. Assessment is by a combination of coursework and examination.

Taught courses

Compulsory courses:

Fundamentals of Data Management (Semester 1)
Message-Passing Programming (Semester 1)
Programming Skills (Semester 1)
Threaded Programming (Semester 1)
Data Analytics with High Performance Computing (Semester 2)
Software Development (Semester 2)
Project Preparation (Semester 2)

Optional courses:

HPC Architectures (Semester 1)
Parallel Numerical Algorithms (Semester 1)
Parallel Programming Languages (Semester 1)
Advanced Parallel Programming (Semester 2)
HPC Ecosystem (Semester 2)
Parallel Design Patterns (Semester 2)
Performance Programming (Semester 2)
Courses from the School of Informatics, Mathematics or Physics (up to 30 credits)
Dissertation

After completing the taught courses, students work on a three-month individual project leading to a dissertation. Dissertation projects may be either research-based or industry-based with an external organisation, with opportunities for placements in local companies.

Industry-based dissertation projects
Through our strong links with industry, we offer our students the opportunity to undertake their dissertation project with one of a wide range of local companies.

An industry-based dissertation project can give you the opportunity to enhance your skills and employability by tackling a real-world project, gaining workplace experience, exploring potential career paths and building relationships with local companies.

Career opportunities

Our graduates are employed across a range of commercial areas, for example software development, petroleum engineering, finance and HPC support. Others have gone on to PhD research in fields that use HPC technologies, including astrophysics, biology, chemistry, geosciences, informatics and materials science.

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To meet the increasing demand for MSc students to have industry experience, we have introduced this new two-year MSc programme. Read more
To meet the increasing demand for MSc students to have industry experience, we have introduced this new two-year MSc programme. Designed for graduates of the highest calibre the MSc will develop advanced knowledge and skills and give you the opportunity to put your knowledge into practice through valuable work experience during a one year industrial placement.

The MSc in Big Data and High Performance Computing provides students with an in-depth understanding of big data analysis and processing using high performance computing technology. Run in conjunction with the STFC Hartree Centre, this MSc programme enables students to gain a specialist qualification in an area of computing that is in great demand worldwide.

The two-year MSc programme shares the same taught modules with its one-year equivalent. However, unlike the one-year MSc which includes an MSc project over the summer, the two-year programme includes an industrial project and placement in year two (either in the UK or overseas). The placement is typically 30 weeks from September to June.

This opportunity to work in industry will help you strengthen your career options by:

Undertaking the project work in an industrial setting
Applying theory learnt in the classroom to real-world practice
Developing communications and interpersonal skills Building networks and knowledge which will be invaluable throughout your career.
During the placement year you will spend time working in a relevant company suitable for the MSc. This is an excellent opportunity to gain practical engineering experience which will boost your CV, build networks and develop confidence in a working environment. Many placement students continue their relationship with the placement provider by undertaking relevant projects and may ultimately return to work for the company when they graduate.

The University of Liverpool has a dedicated team to help students find a suitable placement. Preparation for the placement is provided by the University’s Careers and Employability Service (CES) who assist students in finding a placement, help students produce a professional CV and prepare students for placement interviews.

The University has very good links with industry and several companies work with us to offer our MSc students competitive placements. Although industry placements are not guaranteed, the University offers you opportunities and support throughout the process to ensure that the chance to find a placement is high.

If you are unable to secure a suitable placement by the end of April during year one, you will be transferred onto the one-year MSc to undertake the MSc project over the summer and graduate after one year.

The programme is organised as two taught semesters followed by an individual project undertaken over either the summer or, if desired, during the following year of study. Within each semester students study a number of modules adding up to 60 credits per semester (120 in total). This will be followed by a project dissertation, also 60 credits, making an overall total of 180 credits.

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Take advantage of one of our 100 Master’s Scholarships or College of Science Postgraduate Scholarships to study High Performance and Scientific Computing at Swansea University, the Times Good University Guide’s Welsh University of the Year 2017. Read more
Take advantage of one of our 100 Master’s Scholarships or College of Science Postgraduate Scholarships to study High Performance and Scientific Computing at Swansea University, the Times Good University Guide’s Welsh University of the Year 2017. Postgraduate loans are also available to English and Welsh domiciled students. For more information on fees and funding please visit our website.

The MSc in High Performance and Scientific Computing is for you if you are a graduate in a scientific or engineering discipline and want to specialise in applications of High Performance computing in your chosen scientific area. During your studies in High Performance and Scientific Computing you will develop your computational and scientific knowledge and skills in tandem helping emphasise their inter-dependence.

On the course in High Performance and Scientific Computing you will develop a solid knowledge base of high performance computing tools and concepts with a flexibility in terms of techniques and applications. As s student of the MSc High Performance and Scientific Computing you will take core computational modules in addition to specialising in high performance computing applications in a scientific discipline that defines the route you have chosen (Biosciences, Computer Science, Geography or Physics). You will also be encouraged to take at least one module in a related discipline.

Modules of High Performance and Scientific Computing MSc

The modules you study on the High Performance and Scientific Computing MSc depend on the route you choose and routes are as follows:

Biosciences route (High Performance and Scientific Computing MSc):

Graphics Processor Programming
High Performance Computing in C/C++
Operating Systems and Architectures
Software Testing
Programming in C/C++
Conservation of Aquatic Resources or Environmental Impact Assessment
Ecosystems
Research Project in Environmental Biology
+ 10 credits from optional modules

Computer Science route (High Performance and Scientific Computing MSc):

Graphics Processor Programming
High Performance Computing in C/C++
Operating Systems and Architectures
Software Testing
Programming in C/C++
Partial Differential Equations
Numerics of ODEs and PDEs
Software Engineering
Data Visualization
MSc Project
+ 30 credits from optional modules

Geography route (High Performance and Scientific Computing MSc):

Graphics Processor Programming
High Performance Computing in C/C++
Operating Systems and Architectures
Software Testing
Programming in C/C++
Partial Differential Equations
Numerics of ODEs and PDEs
Modelling Earth Systems or Satellite Remote Sensing or Climate Change – Past, Present and Future or Geographical Information Systems
Research Project
+ 10 credits from optional modules

Physics route (High Performance and Scientific Computing MSc):

Graphics Processor Programming
High Performance Computing in C/C++
Operating Systems and Architectures
Software Testing
Programming in C/C++
Partial Differential Equations
Numerics of ODEs and PDEs
Monte Carlo Methods
Quantum Information Processing
Phase Transitions and Critical Phenomena
Physics Project
+ 20 credits from optional modules

Optional Modules (High Performance and Scientific Computing MSc):

Software Engineering
Data Visualization
Monte Carlo Methods
Quantum Information Processing
Phase Transitions and Critical Phenomena
Modelling Earth Systems
Satellite Remote Sensing
Climate Change – Past, Present and Future
Geographical Information Systems
Conservation of Aquatic Resources
Environmental Impact Assessment
Ecosystems

Facilities

Students of the High Performance and Scientific Computing programme will benefit from the Department that is well-resourced to support research. Swansea physics graduates are more fortunate than most, gaining unique insights into exciting cutting-edge areas of physics due to the specialized research interests of all the teaching staff. This combined with a great staff-student ratio enables individual supervision in advanced final year research projects. Projects range from superconductivity and nano-technology to superstring theory and anti-matter. The success of this programme is apparent in the large proportion of our M.Phys. students who seek to continue with postgraduate programmes in research.

Specialist equipment includes:

a low-energy positron beam with a highfield superconducting magnet for the study of positronium
a number of CW and pulsed laser systems
scanning tunnelling electron and nearfield optical microscopes
a Raman microscope
a 72 CPU parallel cluster
access to the IBM-built ‘Blue C’ Supercomputer at Swansea University and is part of the shared use of the teraflop QCDOC facility based in Edinburgh

The Physics laboratories and teaching rooms were refurbished during 2012 and were officially opened by Professor Lyn Evans, Project Leader of the Large Hadron Collider at CERN. This major refurbishment was made possible through the University’s capital programme, the College of Science, and a generous bequest made to the Physics Department by Dr Gething Morgan Lewis FRSE, an eminent physicist who grew up in Ystalyfera in the Swansea Valley and was educated at Brecon College.

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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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Come to our Postgraduate Open Day on Friday 10 February to find out more!. By studying this Masters course you’ll be well placed to join one of the most performance-driven applications of computer science – the multi-billion pound global games industry. Read more
Come to our Postgraduate Open Day on Friday 10 February to find out more!

By studying this Masters course you’ll be well placed to join one of the most performance-driven applications of computer science – the multi-billion pound global games industry.

As a graduate of this course you will work at the top-end of the games industry and will develop computer graphics on high-performance platforms, or write engines for the next generation of games.

This course will build on your computer science knowledge to specialise in computer graphics, where games programmers must push computing resources to the limit, using deep understanding of architecture and high-performance programming to generate new levels of graphical realism and visual effects on cutting-edge hardware platforms

You can be sure that what you learn will be the technical skills required by industry as this course has been developed in collaboration with a prestigious steering group from industry comprising:

- Barog Game Labs
- Double Eleven
- Epic Games
- NVIDIA
- Team 17
- Sumo Digital
- Weaseltron

During this course you will develop a proficiency in low-level programming (C++, Graphic and Compute shaders), a thorough understanding of multi-core and many-core programming techniques, game engine and tool development techniques, and fundamental insight into graphics and the practical techniques used in games, including geometric models, animation and simulation, and advanced methods for visual realism.

Keywords: games development, gaming, games engineering, graphics, computing, computer science

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Computers are now ubiquitous with many devices and systems being controlled by software. Building robust and reliable software systems requires a deeper knowledge of software design principles and programming methodologies. Read more
Computers are now ubiquitous with many devices and systems being controlled by software. Building robust and reliable software systems requires a deeper knowledge of software design principles and programming methodologies.

The MSc Computing is a full time, one year taught course with a focus on programming and programming related aspects. This is to enable our graduates to go on to a professional career in the computing industry in roles such as team leaders or skilled developers.

The course is designed for students who already possess a degree in IT or related discipline or have equivalent industrial experience, and want to deepen their knowledge in software systems. It covers a range of topics including advanced programming, software engineering and testing, privacy and security, advanced user-interface design and high performance computing.

Course aims
-Advanced Programming: You will gain a thorough grounding of advanced programming concepts using Java, concurrent and real-time programming principles.
-User-Interfaces: You will be introduced to introductory and advanced methods in how users interact with systems (Human-Computer Interaction (HCI)).
-Advanced Software Engineering: You will learn the principles of software engineering using UML, formal methods and software testing.

Learning Outcomes
When you graduate from this course, you will have an in-depth understanding of software systems and programming principles and be able to lead a team of developers in the IT industry. You will have a thorough understanding of:
-Advanced programming knowledge including Java and principles for high performance computing.
-Designing and specifying software components and systems using UML.
-In-depth knowledge of user interface design principles.
-Software testing, privacy and security aspect of software engineering and software management.

Project

The individual project is undertaken by students in Terms 3 and 4 (Summer term and Vacation term). The subject matter of projects varies widely; most projects are suggested by members of staff, some by external organisations, and some by students themselves, allowing students to undertake work relating to an area of personal interest that they wish to develop further.

All project proposals are rigorously vetted and must meet a number of requirements before these are made available to the students. The department uses an automated project allocation system for assigning projects to students that takes into account supervisor and student preferences.

Examples of previous project titles include:
-Autosuggestions using Ajax to improve tag based tactile image retrieval
-An Implementation of Mobile Application in Location-aware Service Domain
-Design and Implementation of a Tool Support for Time Bands Modelling
-Image Anomaly Detection and Object Recognition
-Image retrieval using region of interest detection
-Modelling and Simulation of Business Processes
-Reinforcement Learning for the StarCraft Real-Time Strategy Game
-Software for Autistic Children with Communication Difficulties
-The Design, Implementation, and Safety Analysis of a Mobile ePrescription System
-Using Procedural Content Generation to Provide a Set of Game Challenges During a Single Playthrough

Information for Students

The MSc in Computing course is for those with some background in computing, and so we make some assumptions about your existing knowledge and understanding.

You'll start the course with a focus on writing and developing Java programs. We assume that you are familiar with programming concepts and terminology, so we advise you to review basic programming concepts, such as:
-Variables and their types
-Control structures (e.g. if-statements, loops)
-Subprograms (e.g. procedures, functions)
-Compilation and debugging.

If you have never used Java, you will benefit greatly from doing some reading and trying out Java programming before you arrive. We will teach you from first principles, but the pace will be fast and you will find it easier to keep up if you've practiced with the basics beforehand. Tutorials and practical exercises are the best way for you to prepare, and the Deitel and Deitel book below is a good source of these.

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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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Take advantage of one of our 100 Master’s Scholarships or College of Science Postgraduate Scholarships to study Data Science at Swansea University, the Times Good University Guide’s Welsh University of the Year 2017. Read more
Take advantage of one of our 100 Master’s Scholarships or College of Science Postgraduate Scholarships to study Data Science at Swansea University, the Times Good University Guide’s Welsh University of the Year 2017. Postgraduate loans are also available to English and Welsh domiciled students. For more information on fees and funding please visit our website.

MSc in Data Science aims to equip students with a solid grounding in data science concepts and technologies for extracting information and constructing knowledge from data. Students of the MSc Data Science will study the computational principles, methods, and systems for a variety of real world applications that require mathematical foundations, programming skills, critical thinking, and ingenuity. Development of research skills will be an essential element of the Data Science programme so that students can bring a critical perspective to current data science discipline and apply this to future developments in a rapidly changing technological environment.

Key Features of the MSc Data Science

The MSc Data Science programme focuses on three core technical themes: data mining, machine learning, and visualisation. Data mining is fundamental to data science and the students will learn how to mine both structured data and unstructured data. Students will gain practical data mining experience and will gain a systematic understanding of the fundamental concepts of analysing complex and heterogeneous data. They will be able to manipulate large heterogeneous datasets, from storage to processing, be able to extract information from large datasets, gain experience of data mining algorithms and techniques, and be able to apply them in real world applications. Machine learning has proven to be an effective and exciting technology for data and it is of high value when it comes to employment. Students of the Data Science programme will learn the fundamentals of both conventional and state-of-the-art machine learning techniques, be able to apply the methods and techniques to synthesise solutions using machine learning, and will have the necessary practical skills to apply their understanding to big data problems. We will train students to explore a variety visualisation concepts and techniques for data analysis. Students will be able to apply important concepts in data visualisation, information visualisation, and visual analytics to support data process and knowledge discovery. The students of the Data Science programme also learn important mathematical concepts and methods required by a data scientist. A specifically designed module that is accessible to students with different background will cover the basics of algebra, optimisation techniques, statistics, and so on. More advanced mathematical concepts are integrated in individual modules where necessary.

The MSc Data Science programme delivers the practical components using a number of programming languages and software packages, such as Hadoop, Python, Matlab, C++, OpenGL, OpenCV, and Spark. Students will also be exposed to a range of closely related subject areas, including pattern recognition, high performance computing, GPU processing, computer vision, human computer interaction, and software validation and verification. The delivery of both core and optional modules leverage on the research strength and capacity in the department. The modules are delivered by lecturers who are actively engaged in world leading researches in this field. Students of the Data Science programme will benefit from state-of-the-art materials and contents, and will work on individual degree projects that can be research-led or application driven.

Modules

Modules for the MSc Data Science programme include:

- Visual Analytics
- Data Science Research Methods and Seminars
- Big Data and Data Mining
- Big Data and Machine Learning
- Mathematical Skills for Data Scientists
- Data Visualization
- Human Computer Interaction
- High Performance Computing in C/C++
- Graphics Processor Programming
- Computer Vision and Pattern Recognition
- Modelling and Verification Techniques
- Operating Systems and Architectures

Facilities

The Department of Computer Science is well equipped for teaching, and is continually upgrading its laboratories to ensure equipment is up-to-date – equipment is never more than three years old, and rarely more than two. Currently, our Computer Science students use three fully networked laboratories: one, running Windows; another running Linux; and a project laboratory, containing specialised equipment. These laboratories support a wide range of software, including the programming languages Java, C# and the .net framework, C, C++, Haskell and Prolog among many; integrated programme development environments such as Visual Studio and Netbeans; the widely-used Microsoft Office package; web access tools; and many special purpose software tools including graphical rendering and image manipulation tools; expert system production tools; concurrent system modelling tools; World Wide Web authoring tools; and databases.

As part of the expansion of the Department of Computer Science, we are building the Computational Foundry on our Bay Campus for computer science and mathematical science.

Career Destinations

- Data Analyst
- Data mining Developer
- Machine Learning Developer
- Visual Analytics Developer
- Visualisation Developer
- Visual Computing Software Developer
- Database Developer
- Data Science Researcher
- Computer Vision Developer
- Medical Computing Developer
- Informatics Developer
- Software Engineer

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The migration of more and more business presence and commercial transactions to the internet has produced the need for managers who are familiar with the potential and the limitations of e-commerce, and also for software designers and developers who are aware of the business and marketing environment that sustains them. Read more
The migration of more and more business presence and commercial transactions to the internet has produced the need for managers who are familiar with the potential and the limitations of e-commerce, and also for software designers and developers who are aware of the business and marketing environment that sustains them. The fast past of technology changes requires managers and software designers who can operate comfortably and interchangeably in both worlds.

Why study at Dundee?

The MSc in Information Technology and International Business offers students a practical mix of technical computing modules such as internet and computer systems and software development and business modules like economics for business managers and international business strategy.

The course is based on practical, real-world assignments to provide great experience for your future career. As it is modelled on good practice within the industry, the MSc in Information Technology and International Business ensures that students are kept up-to-date and instilled with a high level of employability.

What's great about this course at Dundee?

International Business at the University of Dundee is well known abroad and as a result, there is a strong multicultural aspect in these classes. Students from countries all over the world like India, China Nigeria and more come here to study giving you the opportunity to work alongside people with a multitude of different ideas, skills and experiences.

The University of Dundee is at the leading edge of computing giving you the opportunity to take advantage of tutelage from leading researchers in an informal and supportive environment.

Your studies will take place in the Queen Mother Building, the University of Dundee’s award-winning epicentre of computing, which boasts an array of conventional and specialised equipment.

Debora Kagohara from Brazil graduated in 2007 from this course, she then went to Victoria University of Wellington, in New Zealand to study for a PhD.

She says "The work was challenging but very enjoyable and the lecturers and tutors were always at hand if I needed help. The degree was not only a good learning experience but I also met great people."

How you will be taught

We know how important it is to be at the leading edge of computing and so you will learn from research-active staff in the School of Computing. Leading researchers teach you and small class sizes mean that they really get to know you, making for an informal and supportive community.

Industrial collaboration is part of our ethos too, so we regularly include guest experts from industry.

What you will study

You select six taught modules, three per semester, during the period September-April. This will typically consist of four computing modules, chosen from:

Software Development
Technology Innovation Management
Database Systems
Software Engineering
Human Computer Interaction and Usability Engineering
Detailed module information for the computing modules is available online.

and two business modules chosen from

Corporate Finance
Performance Management and Reporting
Operations Management and Change
Business Accounting for Non-specialists
Principles of Marketing Practice
International Business Strategy
Global Marketing
Human Resource Management Strategies
Marketing Management and Strategy
Strategic Management Accounting
International Human Resource Management

Subject to examination performance, you then progress to the MSc project which runs from May to September, or to a Diploma project lasting 9 weeks. This is a business-related software development project supervised by a member of staff, culminating in a dissertation.

Please note that some of the modules in the programme are shared with other masters programmes and some of the teaching and resources may be shared with our BSc programme. These joint classes offer a valuable opportunity to learn from, and discuss the material with, other groups of students with different backgrounds and perspectives.

How you will be assessed

The taught modules are assessed by continuous assessment plus end of semester examinations in December and March/April. The project is assessed by dissertation.

Coursework is often very practical, eg writing computer programs, designing interfaces, writing reports, constructing web sites, testing software, implementing databases, analysing problems or presenting solutions to clients.

Careers

Career opportunities in software development, website design, network support, database development and research, working as computer systems manager, data processing manager, software engineer, computer analyst & programmer, computer & IT consultant.

Our students are highly employable:
They develop the expertise that employers want from computing graduates - our Industrial Advisory Board includes experts from a range of industries including Amazon, Scottish Enterprise Tayside, NCR, Chevron and Microsoft

They are prepared for a wide range of good career prospects in computing - the UK faces a massive shortage of graduates qualified to fill the 120,000 new jobs in computing and IT every year

Computing at the University of Dundee is ranked 21st in the UK according to most recent Times Good University Guide and 12th in the UK according to the Guardian University League Table 2009. The University of Dundee has powered its way to a position as one of Scotland's leading universities with an international reputation for excellence across a range of activities. With over 18,000 students, it is growing fast in both size and reputation. It has performed extremely well in both teaching and research assessment exercises, has spawned a range of spin-out companies to exploit its research and has a model wider-access programme.

Dundee has been described as the largest village in Scotland which gives an indication of how friendly and compact it is. With a population of 150,000 it is not too large but has virtually all the cultural and leisure activities you would expect in a much larger city. It is situated beside a broad estuary of the river Tay, surrounded by hills and farmland, and for lovers of the great outdoors it is hard to imagine another UK location that offers so much all year round on land and water. The University is situated in the centre of Dundee, and everything needed is on the one-stop campus: study facilities, help, advice, leisure activities... yet the attractions of the city centre and the cultural quarter are just a stroll away.

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Memorial University’s MSc program in Scientific Computing was one of the first in North America, and remains the only such program in Atlantic Canada. Read more
Memorial University’s MSc program in Scientific Computing was one of the first in North America, and remains the only such program in Atlantic Canada. It trains students in advanced computational techniques and in the application of these techniques to at least one scientific area, such as Applied Mathematics, Chemistry, Computer Science, Earth Sciences, Physics, or Physical Oceanography. Students can expect to gain knowledge and experience in: (1) state-of-the-art numerical methods, (2) high performance computer architectures, (3) use of software development tools for parallel and vector computers, (4) graphics, visualization, and multimedia tools, and (5) acquisition, processing, and analysis of large experimental data sets.

The Scientific Computing program is interdisciplinary, enriched by the expertise of faculty members in a range of academic units. Researchers in external organizations contribute by co-supervising students, providing placements for co-op students, providing computing resources, and teaching some courses. The program has close links with ACEnet, the Atlantic Canada Excellence network of high performance computers on which much of our computational work is carried out.

The program is offered in both thesis and non-thesis (project) versions, with a cooperative education (co-op) option also available. Both full-time and part-time studies are possible. A distinguishing characteristic of this program is the emphasis on interdisciplinary studies. Graduating students will have mastered a broader range of science and engineering areas than graduates from the more traditional, discipline-based programs.

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Memorial University’s MSc program in Scientific Computing was one of the first in North America, and remains the only such program in Atlantic Canada. Read more
Memorial University’s MSc program in Scientific Computing was one of the first in North America, and remains the only such program in Atlantic Canada. It trains students in advanced computational techniques and in the application of these techniques to at least one scientific area, such as Applied Mathematics, Chemistry, Computer Science, Earth Sciences, Physics, or Physical Oceanography. Students can expect to gain knowledge and experience in: (1) state-of-the-art numerical methods, (2) high performance computer architectures, (3) use of software development tools for parallel and vector computers, (4) graphics, visualization, and multimedia tools, and (5) acquisition, processing, and analysis of large experimental data sets.

The Scientific Computing program is interdisciplinary, enriched by the expertise of faculty members in a range of academic units. Researchers in external organizations contribute by co-supervising students, providing placements for co-op students, providing computing resources, and teaching some courses. The program has close links with ACEnet, the Atlantic Canada Excellence network of high performance computers on which much of our computational work is carried out.

The program is offered in both thesis and non-thesis (project) versions, with a cooperative education (co-op) option also available. Both full-time and part-time studies are possible. A distinguishing characteristic of this program is the emphasis on interdisciplinary studies. Graduating students will have mastered a broader range of science and engineering areas than graduates from the more traditional, discipline-based programs.

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Gartner defines Big data as high-volume, high-velocity and high-variety information assets that demand cost-effective, innovative forms of information processing for enhanced insight and decision making. Read more
Gartner defines Big data as high-volume, high-velocity and high-variety information assets that demand cost-effective, innovative forms of information processing for enhanced insight and decision making. A recent IDC forecast shows that the market for Big Data technology and services will grow at a 26.4% compound annual growth rate through 2018, or about six times the growth rate of the overall information technology market.

Saint Mary’s new Master of Science in Computing & Data Analytics (MSc CDA) is a graduate-level, 16-month professional program designed to meet the complex challenges associated with Big Data. It combines two essential aspects of computing and data analytics:

- Software design, development, customization, and management
- Analytics and Business intelligence: the acquisition, storage, management, and analysis of huge amounts of data to improve efficiency, innovation, and decision making

The primary focus of the MSc CDA program is to develop highly qualified computing and data analytics professionals who will drive innovation and organizational success. MSc CDA prepares students for rewarding and lucrative careers in the data science industry through experiential learning opportunities and industry interaction.

Program Structure

In the first two semesters of the MSc CDA program, students are introduced to big data challenges and solutions through eight foundation courses:
- Software Development in Business Environment
- Statistics and its Applications in Business
- Human Computer Interaction
- Managing and Programming Databases
- Business Intelligence
- Managing Information Technology and Systems
- Data Mining
- Web, Mobile, and Cloud Application Development


The second half of the program features three applied learning choices:

- Applied Master Project - System and Functional Analysis / Implementation and Analysis of Results
- Internship
- Research Thesis


Visit smu.ca/academics/msccda-courses.html for course outlines

Benefits of the MSc CDA

- Develop in-demand skills and knowledge that lead to exceptional career opportunities
- Study with award winning instructors from Saint Mary’s Faculty of Science and the Sobey School of Business
- Interact with industry professionals through core courses, paid internships, sponsored projects, industry workshops, expert guest speakers, hackathons, and special events
- MSc CDA studnets create a rich portfolio of apps and software solutions from a wide variety of in-demand platforms (Java/J2EE, C#/.Net, JavaScript/jQuery/jQuery Mobile/node.js, HTML5, PHP, iOS, Android, IBM Bluemix, Azure, SAS, Cognos, SQL/MySQL, NoSQL/Mongo DB, R, Python, IBM Watson)

Why Saint Mary's University

Saint Mary’s approach to learning will uniquely nurture the promise you possess. With one-on-one access to award winning professors, who care about your academic performance and your future, you will have the kind of personal support that makes a difference. Saint Mary’s is located in the historic city of Halifax, the bustling economic and cultural centre of Nova Scotia on Canada's east coast.

Halifax is Atlantic Canada’s Innovation Hub - a leader in the information technology sector with a growing concentration of companies and research organizations focused on analytics innovation.

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Cloud-based systems are the latest development in large-scale computing, and it is predicted that billions of Euros will be spent by European businesses on cloud computing in the coming years. Read more
Cloud-based systems are the latest development in large-scale computing, and it is predicted that billions of Euros will be spent by European businesses on cloud computing in the coming years. But despite this, there are still surprisingly few graduate-level courses in this area.

Our course targets a known need in industry, providing an opportunity for you to study this exciting underlying technology. You gain hands-on experience with various types of cloud models and modern computing systems that use or support cloud computing. You will master areas including:

- Application development for cloud systems
- Cloud management technologies
- System architectures
- High-performance computing
- Social networking application development
- Computer and Network Security

We equip you with the knowledge and understanding to contribute fully to this quickly changing and developing area so that you can enter a range of employment roles related to cloud systems, including developing cloud based applications, managing cloud systems and designing cloud infrastructures.

Our School is a community of scholars leading the way in technological research and development. Today’s computer scientists are creative people who are focused and committed, yet restless and experimental. We are home to many of the world’s top scientists, and our work is driven by creativity and imagination as well as technical excellence.

We are ranked Top 10 in the UK in the 2015 Academic Ranking of World Universities, with more than two-thirds of our research rated ‘world-leading’ or ‘internationally excellent (REF 2014).

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