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Masters Degrees (Machine Vision)

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This specialist option of the . MSc Computational and Software Techniques in Engineering. Read more

This specialist option of the MSc Computational and Software Techniques in Engineering has been developed to deliver qualified engineers to the highest standard into the emerging field of digital signal and image processing who are capable of contributing significantly to this increased demand for both real-time and off-line systems operating over a range of mobile, embedded and workstation platforms.

Who is it for?

Developed for students interested in software development within the wide spectrum of industries in which digital signal processing and/or digital image processing plays a significant role. Suitable for candidates from a broad range of engineering backgrounds, including aeronautical, automotive, mechanical and electrical engineering in addition to the more traditional computational sciences background, who wish to both develop and complement their existing skill-set in this new area. Part-time students have a flexible commencement date.

Why this course?

This option of the MSc in Computational and Software Techniques in Engineering aims to develop your skill-base for the rapidly expanding engineering IT industry sector, not only in the UK but all over the world. Graduates in this option have the opportunity to pursue a wide range of careers embracing telecommunications, the automotive industry, medical imaging, software houses and industrial research where demand for skills is high.

This course additionally forms part of the ESTIA (Ecole Supérieure des Technologies Industrielles Avancées) Cranfield MSc programme which gives ESTIA students the opportunity to study this degree based either at Cranfield University or ESTIA in Bidart, South-West France.

Cranfield University is very well located for visiting part-time students from all over the world, and offers a range of library and support facilities to support your studies. This enables students from all over the world to complete this qualification whilst balancing work/life commitments.

Informed by Industry

The course is directed by an industrial advisory panel who meet twice a year to ensure that it provides the right mix of hands-on skills and up-to-date knowledge suitable for the wide variety of applications that this field addresses.

A number of members also attend the annual student thesis presentations which take place at the end of July. This provides a good opportunity for students to meet key employers.

Course details

The course consists of 12 core modules, including a group design project, plus an individual research project. 

The course is delivered via a combination of structured lectures, tutorial sessions and computer based workshops. Mathematical and computational methods form the basis of the specialist modules, covering the theory and application of DSIP algorithms for the analysis, interpretation and processing of data in diverse fields such as computer vision, robotics, vibro-acoustic condition monitoring, medical diagnosis, remote sensing and data visualisation. This set of specialist modules are designed to provide students with the programming techniques necessary to develop, maintain and use core DSIP solution software over a wide range of industrial settings.

Group project

The group project which takes place in the spring is designed to provide you with invaluable experience of delivering a project within an industry structured team. The project allows you to develop a range of skills including learning how to establish team member roles and responsibilities, project management, delivering technical presentations and gaining experience of working in teams that include members with a variety of expertise and often with members who are based remotely.

Part-time students are encouraged to participate in a group project as it provides a wealth of learning opportunities. However, an option of an individual dissertation is available if agreed with the Course Director.

Recent Group Projects include:

  • Real-time Robotic Sensing
  • Automatic Video Surveillance
  • Face Recognition Systems
  • Applied Digital Signal Processing for Gear Box Analysis
  • Vibro-acoustic Analysis of Turbine Blades.

Individual project

The individual research project allows you to delve deeper into an area of specific interest. It is very common for industrial partners to put forward real world problems or areas of development as potential research project topics. In general you will begin to consider the research project after completing 3-4 modules - it then runs concurrently with the rest of your work.

For part-time students it is common that their research thesis is undertaken in collaboration with their place of work.

Recent Individual Research Projects include:

  • Vision Systems for Real Time Driver Assistance
  • Pattern Recognition for Vibration Analysis
  • Image Stabilisation for UAV Video Footage
  • Presenting Driver Assistance Information Using Augmented Reality
  • Real-time Object Tracking for Intelligent Surveillance Systems
  • 3D Stereo Vision Systems for Robotics and Vehicles.

Assessment

Taught modules 45%, Group project 5%, Individual research project 50%

Your career

The MSc in Computer and Machine Vision attracts enquiries from companies all over the world who wish to recruit high quality graduates. There is considerable demand for students with expertise in engineering software development and for those who have strong technical programming skills in industry standard languages and tools. Graduates of this course will be in demand by commercial engineering software developers, automotive, telecommunications, medical and other industries and research organisations, and have been particularly successful in finding long-term employment.

Some students may go onto degrees, on the basis of their MSc research project. Thesis topics are most often supplied by individual companies on in-company problems with a view to employment after graduation - an approach that is being actively encouraged by a growing number of industries.

A selection of companies that have recruited our graduates include:

  • BAE Systems
  • European Aeronautic Defence and Space Company (EADS)
  • Defence, Science and Technology Laboratory (Dstl)
  • Orange France
  • Microsoft
  • EDS Unigraphics
  • Delcam
  • GKN Technology
  • Logica
  • Oracle Consulting Services
  • National Power
  • Altran Technologies
  • Earth Observation Sciences Ltd
  • Oracle Consulting Services
  • Easams Defence Consultancy
  • Xyratex.


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Developed by the Bristol Robotics Laboratory, this Masters gives students unique exposure to world-leading robotics research, real-life automation and computer vision projects, and the opportunity for placements in UK companies to work on topical industry problems. Read more
Developed by the Bristol Robotics Laboratory, this Masters gives students unique exposure to world-leading robotics research, real-life automation and computer vision projects, and the opportunity for placements in UK companies to work on topical industry problems.

The last 20 years have seen a phenomenal growth in the development and application of computer and machine vision technology. With increasingly complex applications across diverse areas, including manufacturing, security and medicine, there is a growing need for professionals who can evaluate, design and implement technically appropriate and economically viable automation systems for enhancing quality and productivity.

The MSc in Automation and Computer Vision at UWE Bristol is one of the very few postgraduate courses that brings together both of these disciplines into one industry-focused, research-informed Masters.

Key benefits

Some students may be able to do an industry placement as part of their dissertation. Projects will be focused on real problems companies are working on. Those that don't go down the industry route will work at UWE Bristol on a topical research problem.

Course detail

The course provides a unique combination of these two overlapping disciplines, with a strong emphasis on robotics hardware for solving 'real-world' problems. You will develop both the technical knowledge and the business skills needed to introduce advanced automation and machine vision techniques in the workplace.

You will also benefit from the University's close links with industry, with guest lectures on many modules and the chance to work on real-life automation and computer vision projects.

Modules

• Automation and Control (30 credits)
• Machine Vision (30 credits)
• Managing finance (15 credits)
• Project management (15 credits)
• Industrial applications (15 credits)
• Industrial case studies (15 credits)

You will also work on an individual project (60 credits), which forms a major part of the course and gives you the chance to work on real-world research or industry projects

Format

Alongside the strong industry-focus of the course, you will have the opportunity to be part of, and work on, projects in the world-leading Bristol Robotics Laboratory, which brings together influential researchers in service robotics, autonomous systems and bio-engineering.

For those already working, we offer this course as a work-based learning course, as well as a standard full or part-time Masters. Employees of relevant industries can attend part of the course to supplement their existing skills or to be assessed on their current skills and knowledge of these highly topical subject areas.

Assessment

We will make use of a range of types of assessment on the course, including written exams, oral assessments and presentations, reports and project work and written assignments.

Careers / Further study

The course is a good grounding for wider careers in engineering, science, information technology, management and medical imaging. For those wishing to pursue further study, the course is also good preparation for a career in academia or research in fields such as computer vision, robotics, medical imaging, or more general engineering, science and information technology.

How to apply

Information on applications can be found at the following link: http://www1.uwe.ac.uk/study/applyingtouwebristol/postgraduateapplications.aspx

Funding

- New Postgraduate Master's loans for 2016/17 academic year –

The government are introducing a master’s loan scheme, whereby master’s students under 60 can access a loan of up to £10,000 as a contribution towards the cost of their study. This is part of the government’s long-term commitment to enhance support for postgraduate study.

Scholarships and other sources of funding are also available.

More information can be found here: http://www1.uwe.ac.uk/students/feesandfunding/fundingandscholarships/postgraduatefunding.aspx

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This MSc teaches advanced analytical and computational skills for success in a data rich world. Read more

This MSc teaches advanced analytical and computational skills for success in a data rich world. Designed to be both mathematically rigorous and relevant, the programme covers fundamental aspects of machine learning and statistics, with potential options in information retrieval, bioinformatics, quantitative finance, artificial intelligence and machine vision.

About this degree

The programme aims to provide graduates with the foundational principles and the practical experience needed by employers in the area of machine learning and statistics. Graduates of this programme will have had the opportunity to develop their skills by tackling problems related to industrial needs or to leading-edge research.

Students undertake modules to the value of 180 credits.

The programme consists of two core modules (30 credits), four to six optional modules (60 to 90 credits), up to two elective modules (up to 30 credits) and a research project (60 credits). Please note that not all combinations of optional modules will be available due to timetabling restrictions.

Core modules

  • Supervised Learning (15 credits)
  • Statistical Modelling and Data Analysis (15 credits)

Optional modules

Students must choose 15 credits from Group One Options. Of the remaining credits, students must choose a minimum of 30 and a maximum of 60 from Group Two, 15 credits from Group Three and a maximum of 30 credits from Electives.

Group One Options (15 credits)

  • Graphical Models (15 credits)
  • Probabilistic and Unsupervised Learning (15 credits)

Group Two Options (30 to 60 credits)

  • Advanced Deep Learning and Reinforcement Learning (15 credits)
  • Advanced Topics in Machine Learning (15 credits)
  • Applied Machine Learning (15 credits)
  • Approximate Inference and Learning in Probabilistic Models (15 credits)
  • Information Retrieval and Data Mining (15 credits)
  • Introduction to Deep Learning (15 credits)
  • Machine Vision (15 credits)
  • Statistical Natural Language Processing (15 credits)

Group Three Options (15 credits)

  • Applied Bayesian Methods (15 credits)
  • Statistical Design of Investigations (15 credits)
  • Statistical Inference (15 credits)

Please note: the availability and delivery of optional modules may vary, depending on your selection.

A list of acceptable elective modules is available on the Departmental page.

Dissertation/report

All MSc students undertake an independent research project, which culminates in a dissertation of 10,000-12,000 words.

Teaching and learning

The programme is delivered through a combination of lectures, discussions, practical sessions and project work. Student performance is assessed through unseen written examinations, coursework, practical application and the project assessment process.

Further information on modules and degree structure is available on the department website: Computational Statistics and Machine Learning MSc

Careers

There is a strong national and international demand for graduates with skills at the interface of traditional statistics and machine learning. Substantial sectors of UK industry, including leading, large companies already make extensive use of computational statistics and machine learning techniques in the course of their business activities. Globally there are a large number of very successful users of this technology, many located in the UK. Areas in which expertise in statistics and machine learning is in particular demand include: finance, banking, insurance, retail, e-commerce, pharmaceuticals, and computer security. Graduates have gone on to further study at, for example, the Universities of Cambridge, Helsinki, Chicago, as well as at UCL. The MSc is also ideal preparation for a PhD, in statistics, machine learning or a related area.

Recent career destinations for this degree

  • Data Scientist, Interpretive
  • Software Engineer, Google
  • Data Scientist, YouGov
  • Research Engineer, DeepMind
  • PhD in Computer Science, UCL

Employability

Scientific experiments and companies now routinely generate vast databases and machine learning and statistical methodologies are core to their analysis. There is a considerable shortfall in the number of qualified graduates in this area internationally. CSML graduates have been in high demand for PhD positions across the sciences. In London there are many companies looking to understand their customers better who have hired our CSML graduates. Similarly graduates now work in companies in, amongst others, Germany, Iceland, France and the US in large-scale data analysis. The finance sector has also hired several graduates recently.

Careers data is taken from the ‘Destinations of Leavers from Higher Education’ survey undertaken by HESA looking at the destinations of UK and EU students in the 2013–2015 graduating cohorts six months after graduation.

Why study this degree at UCL?

The Centre for Computational Statistics and Machine Learning (CSML) is a major European Centre for machine learning having coordinated the PASCAL European Network of Excellence.

Coupled with the internationally renowned Gatsby Computational Neuroscience and the Machine Learning Unit, and UCL Statistical Science, this MSc programme draws on world-class research and teaching talents. The centre has excellent links with world-leading companies in internet technology, finance and related information areas.

The programme is designed to train students in both the practical and theoretical sides of machine learning. A significant grounding in computational statistics is also provided.

Research Excellence Framework (REF)

The Research Excellence Framework, or REF, is the system for assessing the quality of research in UK higher education institutions. The 2014 REF was carried out by the UK's higher education funding bodies, and the results used to allocate research funding from 2015/16.

The following REF score was awarded to the department: Computer Science

96% rated 4* (‘world-leading’) or 3* (‘internationally excellent’)

Learn more about the scope of UCL's research, and browse case studies, on our Research Impact website.



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Application period/deadline. March 14 - 28, 2018. Research-oriented degree provides an exciting opportunity to study in a leading-edge research environment. Read more

Application period/deadline: March 14 - 28, 2018

• Research-oriented degree provides an exciting opportunity to study in a leading-edge research environment

• The studies combine both theoretical and practical approach

• Specializations in Applied Computing, Artificial Intelligence, and Computer Egineering

The International Master’s Degree Programme in Computer Science and Engineering (CSE) is a two-year research-oriented programme concentrating on intelligent digital solutions to real world problems. During the past decades, Computer Science and Engineering has had a significant impact into our daily lives. The development continues and soon computers will not be used as separate devices anymore. Instead they will blend into our living environments and offer us rich sets of services through natural and intuitive user interfaces. The graduates from Computer Science and Engineering will play a key role in this development.

The two-year programme has three specialisation options:

• Applied Computing

• Artificial Intelligence

• Computer Engineering

Applied Computing focuses on the next generation of interactive systems that place humans at the focus of the technological development. Adopting a multidisciplinary real-world approach, students have to spend a substantial amount of time working in group projects to develop a variety of systems ranging from interactive online services to games and mobile applications, with a strong focus on innovation and design.

Artificial Intelligence focuses in various fields of AI, such as machine learning, machine vision, and data mining. This specialisation provides students with a solid theoretical understanding and practical skills on processing and analyzing digital data and the ability to create intelligent solutions to real world problems with modern AI techniques.

Computer Engineering focuses on both hardware and software aspects of computing with emphasis on embedded system development. In this specialisation, students also study signal processing and its applications, and work with projects on modern signal processors and embedded computers. The specialisation gives the students a good basis to work with Internet of Things (IoT) applications.

In addition to the core specialization options, students can take optional courses to widen their specialization expertise into:

• Biomedical signal analysis

• Machine learning

• Machine vision

• Signal processing

• Embedded systems

• Ubiquitous computing

This Master’s programme is provided by the Faculty of Information Technology and Electrical Engineering, and students are strongly encouraged to work closely with research groups in the faculty that are international leaders in their fields. The Center for Machine Vision and Signal Analysis (CMVS) is renowned world-wide for its 35 years of expertise in computer vision research. The Center for Ubiquitous Computing (UBICOMP) has created a unique research environment for Ubiquitous Computing including multitouch wall-sized displays, smartphone sensing middleware and sensor networks. Biomimetics and Intelligent Systems Group (BISG) is a fusion of expertise from the fields of computer science and biology. During the studies the research groups provide students trainee and master’s thesis positions, with the possibility to continue as a doctoral student, and even as a post-doctoral researcher.

The programme will provide the graduates with sufficient skills to work in a wide variety of positions offered by research institutes and companies mainly operating in the field of information and communications technology (ICT). The graduates are most likely to be employed in research and development related positions, but also management positions and entrepreneurship fit into the profile.

Possible titles include:

• Research Scientist

• Software Engineer

• System Designer

• Project Manager

• Specialist

Students applying for the programme must possess an applicable B.Sc. degree in computer science, electrical engineering or relevant fields such as physics or applied mathematics.

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The fields of graphics, vision and imaging increasingly rely on one another. Read more

The fields of graphics, vision and imaging increasingly rely on one another. This unique and timely MSc provides training in computer graphics, geometry processing, virtual reality, machine vision and imaging technology from world-leading experts, enabling students to specialise in any of these areas and gain a grounding in the others.

About this degree

Graduates will understand the basic mathematical principles underlying the development and application of new techniques in computer graphics and computer vision and will be aware of the range of algorithms and approaches available, and be able to design, develop and evaluate algorithms and methods for new problems, emerging technologies and applications.

Students undertake modules to the value of 180 credits.

The programme consists of four core modules (60 credits), four optional modules (60 credits) and a research project (60 credits).

Core modules

  • Computer Graphics (15 credits)
  • Image Processing (15 credits)
  • Mathematical Methods, Algorithmics and Implementations (15 credits)
  • Research Methods and Reading (15 credits)

Optional modules

Students must choose a minimum of 15 and a maximum of 30 credits from Group One options. Students must choose a minimum of 30 and a maximum of 45 credits from Group Two options.

Group One Options (15 to 30 credits)

  • Machine Vision (15 credits)
  • Virtual Environments (15 credits)

Group Two Options (30 to 45 credits)

  • Acquisition and Processing of 3D Geometry (15 credits)
  • Computational Modelling for Biomedical Imaging (15 credits)
  • Computational Photography and Capture (15 credits)
  • Geometry of Images (15 credits)
  • Graphical Models (15 credits)
  • Information Processing in Medical Imaging (15 credits)
  • Introduction to Machine Learning (15 credits)
  • Inverse Problems in Imaging (15 credits)
  • Numerical Optimisation (15 credits)
  • Robotic Sensing, Manipulation and Interaction (15 credits)
  • Robotic Vision and Navigation (15 credits)

Please note: the availability and delivery of optional modules may vary, depending on your selection.

Dissertation/report

All students undertake an independent research project related to a problem of industrial interest or on a topic near the leading edge of research, which culminates in a 60–80 page dissertation.

Teaching and learning

The programme is delivered through a combination of lectures and tutorials. Lectures are often supported by laboratory work with help from demonstrators. Student performance is assessed by unseen written examinations, coursework and a substantial individual project.

Further information on modules and degree structure is available on the department website: Computer Graphics, Vision and Imaging MSc

Careers

Graduates are ready for employment in a wide range of high-technology companies and will be able to contribute to maintaining and enhancing the UK's position in these important and expanding areas. The MSc provides graduates with the up-to-date technical skills required to support a wealth of research and development opportunities in broad areas of computer science and engineering, such as multimedia applications, medicine, architecture, film animation and computer games. Our market research shows that the leading companies in these areas demand the deep technical knowledge that this programme provides. Graduates have found positions at global companies such as Disney, Sony and Siemens. Others have gone on to PhD programmes at leading universities worldwide.

Recent career destinations for this degree

  • Business Analyst, Adobe
  • Software Engineer, FactSet Research Systems
  • MRes in Engineering, Imperial College London
  • Software Engineer, Sengtian Software
  • PhD in Computer Graphics, UCL

Employability

UCL received the highest percentage (96%) for quality of research in Computer Science and Informatics in the UK's most recent Research Excellence Framework (REF2014).

Our graduates have some of the highest employment rates of any university in the UK. This degree programme also provides a foundation for further PhD study or industrial research.

Careers data is taken from the ‘Destinations of Leavers from Higher Education’ survey undertaken by HESA looking at the destinations of UK and EU students in the 2013–2015 graduating cohorts six months after graduation.

Why study this degree at UCL?

UCL Computer Science contains some of the world's leading researchers in computer graphics, geometry processing, computer vision and virtual environments.

Research activities include geometric acquisition and 3D fabrication, real-time photo-realistic rendering, mixed and augmented reality, face recognition, content-based image-database search, video-texture modelling, depth perception in stereo vision, colour imaging for industrial inspection, mapping brain function and connectivity and tracking for SLAM (simultaneous localisation and mapping).

Research Excellence Framework (REF)

The Research Excellence Framework, or REF, is the system for assessing the quality of research in UK higher education institutions. The 2014 REF was carried out by the UK's higher education funding bodies, and the results used to allocate research funding from 2015/16.

The following REF score was awarded to the department: Computer Science

96% rated 4* (‘world-leading’) or 3* (‘internationally excellent’)

Learn more about the scope of UCL's research, and browse case studies, on our Research Impact website.



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The Machine Learning MSc at UCL is a truly unique programme and provides an excellent environment to study the subject. It introduces the computational, mathematical and business views of machine learning to those who want to upgrade their expertise and portfolio of skills in this domain. Read more

The Machine Learning MSc at UCL is a truly unique programme and provides an excellent environment to study the subject. It introduces the computational, mathematical and business views of machine learning to those who want to upgrade their expertise and portfolio of skills in this domain.

About this degree

Students develop an understanding of the principles underlying the development and application of new techniques in this area, alongside an awareness of, and ability to analyse the range and scope of algorithms and approaches available, and design, develop and evaluate appropriate algorithms and methods for new problems and applications.

Students undertake modules to the value of 180 credits.

The programme consists of one core module (15 credits), five to seven optional modules (75 to 105 credits), up to two modules (30 credits) from electives, and a research project (60 credits).

Core modules

  • Supervised Learning (15 credits)

Optional modules

Students must choose 15 credits from Option Group One and a minimum of 60 credits from Option Group Two. Students must choose a further 30 credits from either Option Group Two or approved electives.

Option Group One (choose 15 credits)

  • Graphical Models (15 credits)
  • Probabilistic and Unsupervised Learning (15 credits)

Option Group Two (choose 60 to 90 credits)

  • Advanced Deep Learning and Reinforcement Learning (15 credits)
  • Advanced Topics in Machine Learning (15 credits)
  • Affective Computing and Human-Robot Interaction (15 credits)
  • Applied Machine Learning (15 credits)
  • Approximate Inference and Learning in Probabilistic Models (15 credits)
  • Bioinformatics (15 credits)
  • Information Retrieval and Data Mining (15 credits)
  • Introduction to Deep Learning (15 credits)
  • Machine Vision (15 credits)
  • Programming and Mathematical Methods for Machine Learning (15 credits)
  • Statistical Natural Language Programming (15 credits)

Please note: the availability and delivery of optional modules may vary, depending on your selection.

Students may select up to 30 credits from elective modules

A list of acceptable elective modules is available on the departmental website.

Dissertation/report

All MSc students undertake an independent research project which culminates in a dissertation of 10,000-12,000 words in the form of a project report.

Teaching and learning

The programme is delivered through a combination of lectures, seminars, class discussions and project supervision. Student performance is assessed though a combination of unseen written examination, coursework (much of which involves programming and/or data analysis), practical application, and the research project.

Further information on modules and degree structure is available on the department website: Machine Learning MSc

Careers

Graduates from this programme have an excellent employment record. Substantial sectors of UK industry, including leading, large companies already make extensive use of intelligent systems techniques in the course of their business activities, and the UK has a number of very successful developers and suppliers of the technology. Students also benefit from strong corporate and academic connections within the UCL Computer Science alumni network.

Graduates have taken machine learning research degrees in domains as diverse as robotics, music, psychology, and bioinformatics at the Universities of Basel, Cambridge, Edinburgh, Nairobi, Oxford and at UCL. Graduates have also found positions with multinational companies such as BAE Systems and BAE Detica.

Recent career destinations for this degree

  • Computer Vision Engineer, ZVR
  • Data Analyst / Data Scientist, Deloitte Data Analytics Group
  • Programmatic Yield Manager and Data Analyst, eBay
  • Data Scientist, dunnhumby
  • PhD in Computer Science, UCL

Employability

Scientific experiments and companies now routinely generate vast databases and machine learning and statistical methodologies are core to their analysis. There is a considerable shortfall in the number of qualified graduates in this area internationally. Machine Learning graduates have been in high demand for PhD positions across the sciences. In London there are many companies looking to understand their customers better who have hired our graduates. Similarly graduates now work in companies in Germany, Iceland, France and the US, amongst other places, in large-scale data analysis. The finance sector has also hired several graduates recently.

Careers data is taken from the ‘Destinations of Leavers from Higher Education’ survey undertaken by HESA looking at the destinations of UK and EU students in the 2013–2015 graduating cohorts six months after graduation.

Why study this degree at UCL?

UCL Computer Science is recognised as a world leader in teaching and research, and our Master's programmes have some of the highest employment rates and starting salaries.

We take an experimental approach to our subject, enjoy the challenge and opportunity of entrepreneurial partnerships and place a high value on our extensive range of industrial collaborations.

This MSc is one of the few leading Master's programmes entirely dedicated to machine learning. It combines a rigorous theoretical academic framework along with specific knowledge of a variety of application fields to fast-track your commercial career or to prepare for PhD research.

Research Excellence Framework (REF)

The Research Excellence Framework, or REF, is the system for assessing the quality of research in UK higher education institutions. The 2014 REF was carried out by the UK's higher education funding bodies, and the results used to allocate research funding from 2015/16.

The following REF score was awarded to the department: Computer Science

96% rated 4* (‘world-leading’) or 3* (‘internationally excellent’)

Learn more about the scope of UCL's research, and browse case studies, on our Research Impact website.



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There is a high demand from industry worldwide, including from substantial sectors in the UK, for graduates with skills at the interface of traditional statistics and machine learning. Read more

There is a high demand from industry worldwide, including from substantial sectors in the UK, for graduates with skills at the interface of traditional statistics and machine learning. MRes graduates benefit from the department's excellent links in finding employment; this programme is also ideal preparation for a research career.

About this degree

The programme aims to provide graduates with the foundational principles and the practical experience needed by employers in the areas of computational statistics and machine learning (CSML). Students will have the opportunity to develop their skills by tackling problems related to industrial needs or to leading-edge research. They also undertake a nine-month research project which enables the department to more fully assess their research potential.

Students undertake modules to the value of 180 credits.

The programme consists of three core modules (30 credits), three optional modules (45 credits) and a dissertation (105 credits).

Core modules

  • Investigating Research
  • Researcher Professional Development

Optional modules

Student select three modules from the following:

  • Advanced Deep Learning and Reinforcement Learning
  • Advanced Topics in Machine Learning
  • Applied Bayesian Methods
  • Approximate Inference and Learning in Probabilistic Models
  • Graphical Models
  • Information Retrieval and Data Mining
  • Introduction to Deep Learning
  • Introduction to Machine Learning
  • Inverse Problems in Imaging
  • Machine Vision
  • Probabilistic and Unsupervised Learning
  • Selected Topics in Statistics
  • Statistical Computing
  • Statistical Inference
  • Statistical Models and Data Analysis
  • Supervised Learning

Dissertation/report

All students undertake an independent research project which culminates in a substantial dissertation.

Teaching and learning

The programme is delivered through a combination of lectures, tutorials and seminars. Lectures are often supported by laboratory work with assistance from demonstrators. Students liaise with their academic or industrial supervisor to choose a study area of mutual interest for the research project. Performance is assessed by unseen written examinations, coursework and the research dissertation.

Further information on modules and degree structure is available on the department website: Computational Statistics and Machine Learning MRes

Careers

Graduates have gone on to further study at, for example, the Universities of Cambridge, Helsinki, and Chicago, as well as at UCL. Similarly, CSML graduates now work in companies in Germany, Iceland, France and the US in large-scale data analysis. The finance sector is also particularly interested in CSML graduates.

Employability

Scientific experiments and companies now routinely generate vast databases, and machine learning and statistical methodologies are core to their analysis. There is a considerable shortfall in the number of qualified graduates in this area internationally, while in London there are many companies looking to understand their customers better who have hired CSML graduates. Computational statistics and machine learning skills are in particular demand in areas including finance, banking, insurance, retail, e-commerce, pharmaceuticals, and computer security. CSML graduates have obtained PhD positions both in machine learning and related large-scale data analysis, and across the sciences.

Why study this degree at UCL?

The Centre for Computational Statistics and Machine Learning (CSML) is a major European Centre for machine learning, having co-ordinated the PASCAL European Network of Excellence which represents the largest network of machine learning researchers in Europe.

UCL Computer Science graduates are particularly valued by the world’s leading organisations in internet technology, finance, and related information areas, as a result of the department’s strong international reputation and ideal location close to the City of London.

Research Excellence Framework (REF)

The Research Excellence Framework, or REF, is the system for assessing the quality of research in UK higher education institutions. The 2014 REF was carried out by the UK's higher education funding bodies, and the results used to allocate research funding from 2015/16.

The following REF score was awarded to the department: Computer Science

96% rated 4* (‘world-leading’) or 3* (‘internationally excellent’)

Learn more about the scope of UCL's research, and browse case studies, on our Research Impact website.



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This degree provides in-depth training for students interested in a career in industry or in research-oriented institutions focused on image and video analysis, and deep learning. Read more

This degree provides in-depth training for students interested in a career in industry or in research-oriented institutions focused on image and video analysis, and deep learning.

State-of-the-art computer-vision and machine-learning approaches for image and video analysis are covered in the course, as well as low-level image processing methods.

Students also have the chance to substantially expand their programming skills through projects they undertake.

Read about the experience of a previous student on this course, Gianmarco Addari.

Programme structure

This programme is studied full-time over 12 months and part-time from 24 to 60 months. It consists of eight taught modules and a standard project. 

Example module listing

The following modules are indicative, reflecting the information available at the time of publication. Please note that not all modules described are compulsory and may be subject to teaching availability and/or student demand.

Technical characteristics of the pathway

This programme in Computer Vision, Robotics and Machine Learning aims to provide a high-quality advanced training in aspects of computer vision for extracting information from image and video content or enhancing its visual quality using machine learning codes.

Computer vision technology uses sophisticated signal processing and data analysis methods to support access to visual information, whether it is for business, security, personal use or entertainment.

The core modules cover the fundamentals of how to represent image and video information digitally, including processing, filtering and feature extraction techniques.

An important aspect of the programme is the software implementation of such processes. Students will be able to tailor their learning experience through selection of elective modules to suit their career aspirations.

Key to the programme is cross-linking between core methods and systems for image and video analysis applications. The programme has strong links to current research in the Department of Electronic Engineering’s Centre for Vision, Speech and Signal Processing.

Facilities, equipment and support

To support your learning, we hold regular MSc group meetings where any aspect of the programme, technical or non-technical, can be discussed in an informal atmosphere. This allows you to raise any problems that you would like to have addressed and encourages peer-based learning and general group discussion.

We provide computing support with any specialised software required during the programme, for example, Matlab. The Faculty’s student common room is also covered by the University’s open-access wireless network, which makes it a very popular location for individual and group work using laptops and mobile devices.

Specialist experimental and research facilities, for computationally demanding projects or those requiring specialist equipment, are provided by the Centre for Vision, Speech and Signal Processing (CVSSP).

Career prospects

Computer vision specialists are be valuable in all industries that require intelligent processing and interpretation of image and video. This includes industries in directly related fields such as:

  • Multimedia indexing and retrieval (Google, Microsoft, Apple)
  • Motion capture (Foundry)
  • Media production (BBC, Foundry)
  • Medical Imaging (Siemens)
  • Security and Defence (BAE, EADS, Qinetiq)
  • Robotics (SSTL)

Studying for Msc degree in Computer Vision offers variety, challenge and stimulation. It is not just the introduction to a rewarding career, but also offers an intellectually demanding and exciting opportunity to break through boundaries in research.

Many of the most remarkable advancements in the past 60 years have only been possible through the curiosity and ingenuity of engineers. Our graduates have a consistently strong record of gaining employment with leading companies.

Employers value the skills and experience that enable our graduates to make a positive contribution in their jobs from day one.

Industrial collaborations

We draw on our industry experience to inform and enrich our teaching, bringing theoretical subjects to life. Our industrial collaborations include:

  • Research and technology transfer projects with industrial partners such as the BBC, Foundry, LionHead and BAE
  • A number of our academics offer MSc projects in collaboration with our industrial partners

Research perspectives

This course gives an excellent preparation for continuing onto PhD studies in computer vision related domains.

Global opportunities

We often give our students the opportunity to acquire international experience during their degrees by taking advantage of our exchange agreements with overseas universities.

In addition to the hugely enjoyable and satisfying experience, time spent abroad adds a distinctive element to your CV.



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Data Science brings together computational and statistical skills and machine learning for data-driven problem solving. Read more

Data Science brings together computational and statistical skills and machine learning for data-driven problem solving. This rapidly expanding area includes deep learning, large-scale data analysis and has applications in e-commerce, search/information retrieval, natural language modelling, finance, bioinformatics and related areas in artificial intelligence.

About this degree

The programme comprises core machine learning methodology and an introduction to statistical science, combined with a set of more specialised and advanced options covering computing and statistical modelling. Projects are offered both within UCL Computer Science and from a range of industry partners.

Students undertake modules to the value of 180 credits.

The programme consists of three compulsory modules (45 credits), four optional modules (75 credits) and a dissertation/report (60 credits).

Core modules

  • Applied Machine Learning (15 credits)
  • Introduction to Machine Learning (15 credits)
  • Introduction to Statistical Data Science (15 credits)

Optional modules

Students must choose 30 credits from Group One options. For the remaining 45 credits, students may choose up to 30 credits from Group Two options or up to 45 credits from Electives.

Group One Options (30 credits)

  • Advanced Deep Learning and Reinforcement Learning (15 credits)
  • Birkbeck College: Cloud Computing (15 credits)
  • Information Retrieval and Data Mining (15 credits)
  • Introduction to Deep Learning (15 credits)
  • Machine Vision (15 credits)
  • Statistical Natural Language Processing (15 credits)
  • Web Economics (15 credits)

Group Two Options (up to 30 credits)

  • Applied Bayesian Methods (15 credits)
  • Decision and Risk (15 credits)
  • Forecasting (15 credits)
  • Statistical Design of Investigations (15 credits)

Electives (up to 45 credits)

  • Affective Computing and Human-Robot Interaction (15 credits)
  • Bioinformatics (15 credits)
  • Computational Modelling for Biomedical Imaging (15 credits)
  • Graphical Models (15 credits)
  • Stochastic Systems (15 credits)
  • Supervised Learning (15 credits)

Please note: the availability and delivery of modules may vary, based on your selected options.

A list of acceptable elective modules is available on the Departmental page.

Dissertation/report

All students undertake an independent research project which culminates in a dissertation of 10,000-12,000 words.

Teaching and learning

The programme is delivered though a combination of lectures, seminars, class discussions and project supervision. Student performance is assessed through a combination of unseen written examination, coursework (much of which involves programming and/or data analysis), practical application, and the research project.

Further information on modules and degree structure is available on the department website: Data Science and Machine Learning MSc

Careers

Data science professionals are increasingly sought after as the integration of statistical and computational analytical tools becomes more essential to organisations. This is a very new degree and information on graduate destinations is not currently available. However, MSc graduates from across the department frequently find roles with major tech and finance companies including:

  • Google Deepmind
  • Microsoft Research
  • Dunnhumby
  • Index Ventures
  • Cisco
  • Deutsche Bank
  • IBM
  • Morgan Stanley

Employability

Students gain a thorough understanding of the fundamentals required from the best practitioners, and the programme's broad base enables data scientists to adapt to rapidly evolving goals.

Why study this degree at UCL?

UCL received the highest percentage (96%) for quality of research in Computer Science and Informatics in the UK's most recent Research Excellence Framework (REF2014).

UCL Computer Science staff have research interests ranging from foundational machine learning and large-scale data analysis to commercial aspect of business intelligence. Our extensive links to companies provide students with opportunities to carry out the research project with an industry partner.

The department also enjoys strong collaborative relationships across UCL; exposure to interdisciplinary research spanning UCL Computer Science and UCL Statistical Science will provide students with a broad perspective of the field. UCL is home to regular machine learning masterclasses and big data seminars.

Research Excellence Framework (REF)

The Research Excellence Framework, or REF, is the system for assessing the quality of research in UK higher education institutions. The 2014 REF was carried out by the UK's higher education funding bodies, and the results used to allocate research funding from 2015/16.

The following REF score was awarded to the department: Computer Science

96% rated 4* (‘world-leading’) or 3* (‘internationally excellent’)

Learn more about the scope of UCL's research, and browse case studies, on our Research Impact website.



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Automation, control and robotics are pervasive enabling technologies found in almost every modern technical system, particularly in manufacturing and production. Read more

Automation, control and robotics are pervasive enabling technologies found in almost every modern technical system, particularly in manufacturing and production. They combine the diverse and rapidly expanding disciplines of automation, control, mechanics, software and signal processing.

This course is ideal if you wish to develop comprehensive knowledge and understanding of • classical and modern control theory • industrial automation • systems analysis • design and simulation • robotics.

You gain the ability to apply principles of modelling, classical and modern control concepts and controller design packages in various areas of industry. You also learn how to design and exploit automation and robotic systems in a range of manufacturing and industrial applications.

The course has six core modules which cover the major aspects of industrial automation and control systems engineering and robotics, ranging from classical linear control system design to non-linear, optimal and intelligent control systems, including distributed control systems, robotics, computer networks and artificial intelligence.

You also choose two optional modules relevant to automation and control to suit your interests. For example, if you wish to work in the manufacturing industry you can choose manufacturing systems or machine vision. There is the opportunity to study one or two management modules if you wish to apply yourself to a more managerial role.

To gain the masters you complete a major research-based project, which can be focused on an area of your particular interest or career need.

You work alongside staff from the Electrical, Electronic and Control Engineering Group and the Centre for Automation and Robotics Research (CARR) at Sheffield Hallam. This provides the opportunity to work with active researchers.

Professional recognition

This course is seeking accreditation by the Institution of Engineering and Technology (IET) on behalf of the Engineering Council for the purposes of fully meeting the academic requirements for registration as a Chartered Engineer. The MSc will meet, in part, the exemplifying academic benchmark requirements for registration as a Chartered Engineer; graduates who have a BEng (Hons) accredited for CEng will be able to show that they have satisfied the further learning requirement for CEng accreditation.

Course structure

Core modules

  • industrial automation
  • control of linear systems
  • advanced control methods
  • robotics
  • applicable artificial intelligence

Options

Choose two from

  • software engineering
  • computer networks
  • project and quality management
  • sustainability, energy and environmental management
  • machine vision
  • digital signals processing
  • manufacturing systems
  • mixed signal design
  • electrical energy systems
  • efficient machines and electromagnetic applications.

MSc

  • project and dissertation

Assessment

  • coursework
  • examination
  • presentation
  • MSc project report

Employability

This course provides you with the knowledge and skills for further advanced study in this area.

You can also apply your skills in an industrial setting for automated manufacturing, control system design, or in the wide range of industries that exploit intelligent robotics. Graduates from this course find career opportunities in areas including • automation and control • process and petrochemical • biomedical • manufacturing • energy • automotive • aerospace.

You can also pursue careers in engineering design and development, engineering research, engineering consultancy and engineering management.

Completing this course combined with further work-based experience enables you to gain Chartered Engineer status.



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This programme aims to give students professional knowledge and skills for a successful career in the future that are orientated to engineering technologies… Read more

This programme aims to give students professional knowledge and skills for a successful career in the future that are orientated to engineering technologies, such as mechatronics, robotics and automation, which presume professional abilities to integrate, conduct and lead complex engineering projects integrating ICT and hardware technologies for solving practical problems and starting a new business. This programme is strongly orientated towards practical, hands-on projects and developing practical skills supported by laboratories of mechatronics, robotics, machine vision, metrology and measurement technology and computer classes.

Most of the courses are project based supported by theoretical materals. There is a project course on the first year containing the topics of machine vision and mechatronics systems aimed to support development of a real automation or robotic systems by student groups ending with presenting a conference paper as a rule. Examples of this kind projects are „Quadrocopter gesture control sytem“, „Automated inspection system of electric motor staator and rootor sheets“ and „Development of the camera system for the nanosatellite“, etc.

Future belongs to the specialities which provide flexible and integrated deep technical knowledge as it is the case in Mechatronics curricula. A good proof of this is the fact that Forbes nominates two MSc Mechatronics graduates among the EU most successful young leaders and entrepreneurs in Industry and Science in 2015.

Key features

  • The programme offers a unique opportunity to acquire integrated knowledge and skills in electrical/electronics and mechanical engineering along with deep ICT knowledge by the courses from the faculties of Mechanical Engineering, Information Technology and Power Engineering
  • Strong orientation towards facilitating the development of business and entrepreneurial skills, supported by strong practical course projects
  • The programme conforms to internationally recognized professional mechatronics engineering qualification standards
  • Double Degree option in MSc Mechatronics in collaboration with ITMO University St. Petersburg http://en.ifmo.ru/
  • Semester or internship abroad in world leading universities by support of Erasmus+ programme
  • Scholarships available for the best students

Curriculum

Structure of curriculum

Future career options

The graduates of the programme possess skills applicable in a broad sphere, such as automation and robotics engineers in industry; project managers and entrepreneurs and managers of innovative SMEs orientated towards developing cutting edge technology or medicinal devices; health and space industry; security; products combining hardware and ICT, etc. Active and bright mechatronics engineers are urgently needed in the fields of automatic production lines and robotics such as ABB or SIEMENS. Students can also continue their studies at PhD level in mechatronics or in a related field.



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The Institute of Perception, Action and Behaviour (IPAB) focuses on how to link computational perception, representation, transformation and generation processes to external worlds, in theory and in practice. Read more

The Institute of Perception, Action and Behaviour (IPAB) focuses on how to link computational perception, representation, transformation and generation processes to external worlds, in theory and in practice.

This covers domains such as visual perception, dynamic control of robot systems, active sensing and decision making, biomimetic robotics, computer-based generation of external phenomena, such as images, music or actions, and agent-based interaction within computer games and animation.

Supported by the dynamic research culture of IPAB, you can develop robots that learn their own motor control, mimic animal behaviours, or produce autonomous and coordinated team actions. Or you can work with systems that interpret real images and video, or generate complex behaviour in animated characters.

We aim to link strong theoretical perspectives with practical hands-on construction, and provide the hardware and software support to realise this vision.

Training and support

You carry out your research within a research group under the guidance of a supervisor. You will be expected to attend seminars and meetings of relevant research groups and may also attend lectures that are relevant to your research topic. Periodic reviews of your progress will be conducted to assist with research planning.

A programme of transferable skills courses facilitates broader professional development in a wide range of topics, from writing and presentation skills to entrepreneurship and career strategies.

The School of Informatics holds a Silver Athena SWAN award, in recognition of our commitment to advance the representation of women in science, mathematics, engineering and technology. The School is deploying a range of strategies to help female staff and students of all stages in their careers and we seek regular feedback from our research community on our performance.

Facilities

Our robotics labs contain a range of mobile platforms, robot manipulators, humanoid robots, and custom-built sensor and actuation systems that attract continuous interest from funders, industry and members of the public.

Recent developments include the UK's only NASA Valkyrie robot platform, application of robotic hardware to prosthetics and assisted living, and a team that competes in the international robot soccer league.

Our new Edinburgh Centre for Robotics (ECR) brings collaboration with Heriot-Watt University to expand the range of facilities and applications we can explore, and to fund research training.

The machine vision lab has facilities for 3D range data capture, motion capture and high-resolution and high-speed video, and the high performance computing needed for graphics is well supported, including hardware partnerships with companies such as NVIDIA.

Career opportunities

While many of our graduates go on to highly successful academic careers, others find their niche in commercial research labs, putting their knowledge and skills to use in an industry setting.

Several of our recent graduates have set up or joined spin-out robotics companies. Our graphics researchers have strong connections to the media and games industries.



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Data Science brings together computational and statistical skills for data-driven problem solving. Read more
Data Science brings together computational and statistical skills for data-driven problem solving. This rapidly expanding area includes machine learning, deep learning, large-scale data analysis and has applications in e-commerce, search/information retrieval, natural language modelling, finance, bioinformatics and related areas in artificial intelligence.

Degree information

The programme comprises core machine learning methodology and an introduction to statistical science, combined with a set of more specialised and advanced options covering computing and statistical modelling. Projects are offered both within UCL Computer Science and from a wide range of industry partners.

Students undertake modules to the value of 180 credits.

The programme consists of three compulsory modules (45 credits), five optional modules (75credits) and a dissertation/report (60 credits).

Core modules
-Applied Machine Learning
-Introduction to Supervised Learning
-Introduction to Statistical Data Science

Optional modules - students choose a minimum of 30 credits and a maximum of 60 credits from the following optional modules:
-Cloud Computing (Birkbeck)
-Machine Vision
-Information Retrieval & Data Mining
-Statistical Natural Language Processing
-Web Economics

Students choose a minimum of 0 credits and a maximum of 30 credits from these optional Statistics modules:
-Statistical Design of Investigations
-Applied Bayesian Methods
-Decision & Risk

Students choose a minimum of 15 credits and a maximum of 15 credits from these elective modules:
-Supervised Learning
-Graphical Models
-Bioinformatics
-Affective Computing and Human-Robot Interaction
-Computational Modelling for Biomedical Imaging
-Stochastic Systems
-Forecasting

Dissertation/report
All students undertake an independent research project which culminates in a dissertation of 10,000-12,000 words.

Teaching and learning
The programme is delivered though a combination of lectures, seminars, class discussions and project supervision. Student performance is assessed through a combination of unseen written examination, coursework (much of which involves programming and/or data analysis), practical application, and the research project.

Careers

Data science professionals are increasingly sought after as the integration of statistical and computational analytical tools becomes more essential to organisations. A thorough understanding of the fundamentals required from the best practitioners, and this programme's broad base, assists data scientists to adapt to rapidly evolving goals. This is a new degree and information on graduate destinations is not currently available. However, MSc graduates from across the department frequently find roles with major tech and finance companies including:
-Google Deepmind
-Microsoft Research
-Dunnhumby
-Index Ventures
-Last.fm
-Cisco
-Deutsche Bank
-IBM
-Morgan Stanley

Why study this degree at UCL?

The 2014 Research Excellence Framework ranked UCL first in the UK for computer science. 61% of its research work is rated as world-leading and 96% as internationally excellent.

UCL Computer Science staff have research interests ranging from foundational machine learning and large-scale data analysis to commercial aspect of business intelligence. Our extensive links to companies provide students with opportunities to carry out the research project with an industry partner.

The department also enjoys strong collaborative relationships across UCL; and exposure to interdisciplinary research spanning UCL Computer Science and UCl Statistical Science will provide students with a broad perspective of the field. UCL is home to regular machine learning masterclasses and big data seminars.

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Learn how to research, design and develop machine learning and autonomous systems technologies. You’ll be prepared for a wide range of careers in industry. Read more

Learn how to research, design and develop machine learning and autonomous systems technologies. You’ll be prepared for a wide range of careers in industry.

Intelligent and autonomous systems are increasingly important in all areas of human life and activity from medicine and space exploration to agriculture and entertainment.

Understanding and developing autonomous systems involves a range of skills and knowledge including designing interactive systems with both human and machine elements, and modelling and building systems that can sense and learn.

Machine learning is at the heart of autonomous and intelligent systems, including computer vision and robotics. It also underpins the recent developments in data analytics across many fields.

You will learn to use new knowledge to solve complex machine learning and autonomous systems problems. You’ll develop a range of skills including the theory of machine learning, artificial intelligence, autonomous systems design and engineering, and the implications for humans of interacting more and more with intelligent and autonomous systems.

You will be taught by academics from the Department of Computer Science with expertise in machine learning, autonomous systems, artificial intelligence and human-computer interaction. This course has been designed in collaboration with the Department of Electronic and Electrical Engineering who offer expertise in robotics.

You will study in a research-led department with a supportive postgraduate community. You’ll learn in our bespoke computer laboratory and be exposed to the latest ideas and technology. The department has strong links to industry both nationally and internationally.

With machine learning and autonomous systems forming an essential part of a number of key industries, our MSc graduates will be highly sought after by employers.

You’ll gain the knowledge and transferable skills for a career in a wide range of industries, or for further study at PhD level. Graduates from the department have gone on to work in a wide variety of sectors, including IT consultancy, software development, banking and education.

Visit the website.



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Engineering software development is one of the key areas in the European information technology sector. It is a fast moving subject of crucial importance to industry and forms the basis for a wide and ever growing variety of applications. Read more

Engineering software development is one of the key areas in the European information technology sector. It is a fast moving subject of crucial importance to industry and forms the basis for a wide and ever growing variety of applications.

This course with its blend of skills-based and subject specific material, has the fundamental objective of equipping you with the generic hands-on skills and up-to-date knowledge adaptable to the wide variety of applications that this field addresses.

Choose from three specialist options:

Who is it for?

If you intend to make a career in software development, whether it is in the data centre, on the desktop or in the rapidly expanding mobile application space, you need to have a strong basis in software engineering. The MSc in Computational and Software Techniques in Engineering is unique in that it combines software engineering with high performance computing, giving you the tools and techniques that employers are looking for and an advantage in the job market.

Why this course?

This course produces well qualified graduates, ready to take on professional roles without additional training on the job. Due to this, our graduates are in high demand with industry leaders visiting Cranfield to showcase their graduate roles.

In addition to the software/computational topics, we deliver a core module entitled Management for Technology, which focuses on those aspects of management which will enable you to fulfil a wider role in an organisation more effectively.

We are very well located for visiting part-time students from all over the world, and offers a range of library and support facilities to support your studies. This enables students from all over the world to complete this qualification whilst balancing work/life commitments.This Msc programme benefits from a wide range of cultural backgrounds which significantly enhances the learning experience for both staff and students.

Informed by Industry

The course is directed by an industrial advisory panel who meet twice a year to ensure that it provides the right mix of hands-on skills and up-to-date knowledge suitable for to the wide variety of applications that this field addresses.

A number of members also attend the annual student thesis presentations which take place at the end of July, a month or so before the end of the course. This provides a good opportunity for students to meet key employers.

Course details

You will complete four compulsory modules followed by specialist modules from your selected MSc option. In addition to the taught component, you will complete a group project and an individual research project.

Group project

The group design project is intended to provide you with invaluable experience of delivering a project within an industry structured team. The project allows you to develop a range of skills including learning how to establish team member roles and responsibilities, project management, delivering technical presentations and gaining experience of working in teams that include members with a variety of expertise and often with members who are based remotely.

Part-time students are encouraged to participate in a group project as it provides a wealth of learning opportunities. However, an option of an individual dissertation is available if agreed with the Course Director.

Group Project subject areas include:

  • Applications of Computational Engineering Design
  • Applications of DSP and Computer Vision
  • Applications in High-End Computing.

Individual project

The individual research project allows you to delve deeper into an area of specific interest. It is very common for industrial partners to put forward real world problems or areas of development as potential research thesis topics. For part-time students it is common that their research thesis is undertaken in collaboration with their place of work.

Assessment

Taught modules 40%, Group project 10%, Individual research project 50%

Your career

The MSc in Computational and Software Techniques in Engineering is designed to equip you with the skills required to pursue a successful career working in the UK and overseas. This course attracts enquiries from companies in the rapidly expanding engineering IT industry sector across the world who wish to recruit high quality graduates.

This course is meeting the industry demand for personnel with expertise in engineering software development and for those who have strong technical programming skills in industry standard languages and tools.

Some of our graduates go onto PhD degrees. Project topics are most often supplied by individual companies on in-company problems with a view to employment after graduation – an approach that is being actively encouraged by a growing number of industries.



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