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Masters Degrees (Vision Science)

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Our MSc/MRes Investigative Ophthalmology and Vision Sciences course brings together the research expertise in vision from The University of Manchester and the clinical expertise of . Read more

Our MSc/MRes Investigative Ophthalmology and Vision Sciences course brings together the research expertise in vision from The University of Manchester and the clinical expertise of Manchester Royal Eye Hospital .

The course is aimed at optometrists, ophthalmologists, orthoptists and nurses from the UK and overseas. It is suitable for:

  • individuals who are considering undertaking a research degree in the vision sciences;
  • those interested in professional development;
  • those interested in conducting research as part of their clinical training;
  • ophthalmologists wishing to expand and extend their training into specialist areas;
  • optometrists considering a career in the hospital eye service.

This course will provide you with a firm grounding in the knowledge needed to pursue a higher degree and to conduct high quality research in ophthalmology, optometry or vision sciences.

It also gives an opportunity for vision-related professionals to advance their knowledge of the scientific foundations of ophthalmology and vision sciences.

Aims

This course aims to provide those working within the ophthalmic professions (ophthalmologists, optometrists, vision scientists, orthoptists and ophthalmic nurses) with an opportunity for professional development.

It will give you a firm grounding in the knowledge, understanding and skills you will need to pursue a higher research degree or to participate in research programmes and meet a need for researchers who can form a bridge between basic research and applied clinical research.

Through the literature review and dissertation, you will develop skills of systematically analysing and interpreting a body of literature, designing and conducting a research project, and analysing and presenting research findings within a written dissertation.

Teaching and learning

In each unit, learning will be based on a series of formal lectures on topics relating to ocular disease and treatments, and a series of more informal tutorials on current research. You will receive copies of presentations and direction to relevant literature for personal study.

Many dissertation projects have led to peer-reviewed publications in ophthalmic literature. Recent titles include the following.

  • Optical coherence tomography measures of the retinal nerve fibre layer.
  • Development of a model cell assay to investigate the cellular processing of ARB mutant bestrophin-1.
  • Risk factors for late presentation of patients with primary open angle glaucoma.
  • Molecular analysis of autosomal recessive retinal dystrophies.
  • In vivo analysis of the wettability of silicon hydrogel contact lenses.
  • Can corneal densitometry be used to assess the treatment outcome after corneal transplantation.
  • A contact lens based technique delivering cultured stem cells onto the human corneal surface.
  • The use of corneal imaging to assessing treatment outcomes of LASIK and LASEK.
  • Addressing the physiological cues needed for trans-differentiation of dental pulp stem cells into limbal stem cells.

The course directors are Prof Tariq Aslam and Dr Chantal Hillarby .

Coursework and assessment

Assessment is via:

  • written examinations in January and May;
  • coursework set during the taught units;
  • a research project dissertation.

Course unit details

The course has two different pathways:

  • MSc: Six taught units (15 credits each) and a dissertation (90 credits).
  • MRes: Four taught units (15 credits each), a literature review (30 credits) and a dissertation (90) credits.

The six units are Macular Degeneration, Paediatric Ophthalmology, Cornea, Contact Lens, Vascular Disease and Glaucoma.

What our students say

IOVS is a great course overall; excellent content and very enjoyable. (Abid Ali, ophthalmology trainee [UK])

I've enjoyed the insight into new and modern treatments and diagnostic techniques. (Isaac Nunoo, optometrist [Ghana])

I love the way the lecturers teach and explain, and the ease with which you can access information.(Chimdi Emma-Duru, optometrist and PhD student [Nigeria])

Facilities

Ophthalmology is housed within the Manchester Royal Eye Hospital, which is located on the CMFT site at the southern end of the University campus. Optometry is housed within the Carys Bannister Building. The two sites are few hundred yards apart.

Most dissertations are conducted within the confines of the University and the Manchester Royal Eye Hospital. Students may, however, embark on work outside these confines (eg an optometric practice or other hospital). This is contingent on the acceptance of the research proposal and the approval of suitable external and internal supervisors by the course director.

You will also have access to a range of library and IT facilities across the University.

Disability support

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

CPD opportunities

We offer a number of CPD courses in ophthalmology and optometry .

Career opportunities

This MSc is aimed at optometrists, ophthalmologists, orthoptists, biological scientists, nurses and those from related backgrounds, and can open up a number of career opportunities.

The course is suitable if you want to further your knowledge of the vision sciences or if you are an optometrist considering professional development or a career in the hospital eye service.

It is also ideal if you want to conduct research as part of your clinical training or pursue an academic career in ophthalmology, optometry and the vision sciences.



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The Department of Health has introduced a new career pathway for Healthcare Scientists. This includes clinical science training to provide an appropriately trained workforce to work in the NHS. Read more
The Department of Health has introduced a new career pathway for Healthcare Scientists. This includes clinical science training to provide an appropriately trained workforce to work in the NHS.

DBS & Fitness to Practise

Students enrolling onto certain programmes within the School of Life and Health Sciences at Aston University will be required to undertake an Enhanced Level Disclosure and Barring Service check. Also in line with national requirements for programmes leading to a health professional qualification, a number of degree programmes in the School of Life and Health Science are subject to Fitness to Practise Regulations.

Course Outline & Modules

The Department of Health has introduced a new career pathway for Healthcare Scientists. This includes clinical science training to provide an appropriately trained workforce to work in the NHS. This requires the education and training of practitioners in the division of neurosensory science. This division is made up of 3 pathways:
-Audiology
-Neurophysiology
-Ophthalmic & Vision Sciences

Students undertake common learning throughout the programme but also have specialist topics in year two and three. Students are employed within NHS Departments and released to undertake academic study.

First year modules:
-NS1HS1 Introduction to Healthcare Science (15 credits)
-NS1PP1 Professional Practice (15 credits)
-NS1NS1 Introduction to Neurosensory Sciences (15 credits)
-NS1CS1 Clinical Science (15 credits)

Second year modules:
-NS2RM1 Research Methods (10 credits)
-NS2EB1 Evidence Based Practice (10 credits)
-NS2RP1 Research Project (20 credits)

Audiology pathway
-NS2AR1 Adult Rehabilitation (20 credits)

Vision Science pathway
-NS2VS1 Ophthalmic & Vision Science (20 credits)

Neurophysiology pathway
-NS2NE1 Evoked Potentials (20 credits)

Third year module:
Neurophysiology pathway
-Neurophysiology Practice (30 credits)

Learning, teaching & Assesment

The majority of the teaching and learning material is delivered via the virtual learning environment (Blackboard 9). Students are provided with a study guide for each module which ensures they are aware of what material needs covering when. The first year is mostly assessed via coursework so students are able to benchmark their abilities early on and to develop their skills in managing their learning.

In the second and third years of the programme students undertake a research project with an associated research methods module to develop their skills in this area.

Students are on campus for short periods each term when they have the opportunity to participate in group activities, tutorials, skills laboratories and seminars.

Professional accreditation

The programme is accredited by the Department of Health via the Modernising Scientific Careers programme.

Career opportunities

This programme sits within the Department of Health’s vision for the Healthcare Science workforce. The aim is to develop practitioners who can improve the scientific profile within healthcare and who have the requisite skills to enhance both the diagnosis and treatment of patients in the NHS. As such it is predominantly aimed at graduates employed within the Scientist Training Programme. It is also aimed at NHS practitioners within the disciplines who wish to undertake an academic qualification as part of their professional development. Individual modules can be undertaken as part of the School’s flexible credit accumulation system leading to a post-graduate certificate, diploma or masters qualification.

The ageing population means that demand for assessment and treatment services is set to rise substantially over the coming years. Our Graduates will be well placed to enter careers in hospitals, community-based practice and also related research areas. Previous graduates have become advanced practitioners or gone on to lead a section of service.

The programme is designed to formally meet the requirements of the NHS and builds on Aston’s established links and extensive experience of health education.

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Color science is broadly interdisciplinary, encompassing physics, chemistry, physiology, statistics, computer science, and psychology. Read more

Program overview

Color science is broadly interdisciplinary, encompassing physics, chemistry, physiology, statistics, computer science, and psychology. The curriculum, leading to a master of science degree in color science, educates students using a broad interdisciplinary approach. This is the only graduate program in the country devoted to this discipline and it is designed for students whose undergraduate majors are in physics, chemistry, imaging science, computer science, electrical engineering, experimental psychology, physiology, or any discipline pertaining to the quantitative description of color. Graduates are in high demand and have accepted industrial positions in electronic imaging, color instrumentation, colorant formulation, and basic and applied research. Companies that have hired graduates include Apple Inc., Benjamin Moore, Canon Corp., Dolby Laboratories, Eastman Kodak Co., Hallmark, Hewlett Packard Corp., Microsoft Corp., Pantone, Qualcomm Inc., Ricoh Innovations Inc., Samsung, and Xerox Corp.

The color science degree provides graduate-level study in both theory and practical application. The program gives students a broad exposure to the field of color and affords them the unique opportunity of specializing in an area appropriate for their background and interest. This objective will be accomplished through the program’s core courses, selection of electives, and completion of a thesis or graduate project.The program revolves around the activities of the Munsell Color Science Laboratory within the College of Science. The Munsell Laboratory is the pre-eminent academic laboratory in the country devoted to color science. Research is currently under way in color appearance models, lighting, image-quality, color-tolerance psychophysics, spectral-based image capture, archiving, reproduction of artwork, color management, computer graphics; and material appearance. The Munsell Laboratory has many contacts that provide students with summer and full-time job opportunities across the United States and abroad.

Plan of study

Students must earn 30 semester credit hours as a graduate student to earn the master of science degree. For full-time students, the program requires three to four semesters of study. Part-time students generally require two to four years of study. The curriculum is a combination of required courses in color science, elective courses appropriate for the candidate’s background, and either a research thesis or graduate project. Students require approval of the program director if they wish to complete a graduate project, rather than a research thesis, at the conclusion of their degree.

Prerequisites: The foundation program

The color science program is designed for the candidate with an undergraduate degree in a scientific or other technical discipline. Candidates with adequate undergraduate work in related sciences start the program as matriculated graduate students. Candidates without adequate undergraduate work in related sciences must take foundation courses prior to matriculation into the graduate program. A written agreement between the candidate and the program coordinator will identify the required foundation courses. Foundation courses must be completed with an overall B average before a student can matriculate into the graduate program. A maximum of 9 graduate-level credit hours may be taken prior to matriculation into the graduate program. The foundation courses, representative of those often required, are as follows: one year of calculus, one year of college physics (with laboratory), one course in computer programming, one course in matrix algebra, one course in statistics, and one course in introductory psychology. Other science courses (with laboratory) might be substituted for physics.

Curriculum

Color science, MS degree, typical course sequence:
First Year
-Principles of Color Science
-Computational Vision Science
-Historical Research Perspectives
-Color Physics and Applications
-Modeling Visual Perception
-Research and Publication Methods
-Electives
Second Year
-Research
-Electives

Other admission requirements

-Submit scores from the Graduate Record Examination (GRE).
-Submit official transcripts (in English) for all previously completed undergraduate and graduate course work.
-Submit two professional recommendations.
-Complete an on-campus interview (when possible).
-Have an average GPA of 3.0 or higher.
-Have completed foundation course work with GPA of 3.0 or higher (if required), and complete a graduate application.
-International applicants who native language is not English must submit scores from the Test of English as a Foreign Language. Minimum scores of 94 (internet-based) are required. International English Language Testing System (IELTS) scores will be accepted in place of the TOEFL exam. Minimum scores will vary; however, the absolute minimum score required for unconditional acceptance is 7.0. For additional information about the IELTS, please visit http://www.ielts.org.

Additional information

Scholarships and assistantships:
Students seeking RIT-funded scholarships and assistantships should apply to the Color Science Ph.D. program (which is identical to the MS program in the first two years). Currently, assistantships are only available for qualified color science applicants to the Ph.D. program. Applicants seeking financial assistance from RIT must submit all application documents to the Office of Graduate Enrollment Services by January 15 for the next academic year.

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

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

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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Technologies based on the intelligent use of data are leading to great changes in our everyday life. Data Science and Engineering refers to the know-how and competence required to effectively manage and analyse the massive amount of data available in a wide range of domains. Read more
Technologies based on the intelligent use of data are leading to great changes in our everyday life. Data Science and Engineering refers to the know-how and competence required to effectively manage and analyse the massive amount of data available in a wide range of domains.

We offer a two-year Master of Science in Computer Science centered on this emerging field. The backbone of the program is constituted by three core units on advanced data management, machine learning, and high performance computing. Leveraging on the expertise of our faculty, the rest of the program is organised in four tracks, Business Intelligence, Health & Life Sciences, Pervasive Computing, and Visual Computing, each providing a solid grounding in data science and engineering as well as a firm grasp of the domain of interest.

By blending standard classes with recitations and lab sessions our program ensures that each student masters the theoretical foundations and acquires hands-on experience in each subject. In most units credit is obtained by working on a final project. Additional credit is also gained through short-term internship in the industry or in a research lab. The master thesis is worth 25% of the total credit.

TRACKS

• Business Intelligence. This track builds on first hand knowledge of business management and fundamentals of data warehousing, and focuses on data mining, graph analytics, information visualisation, and issues related to data protection and privacy.
• Health & Life Sciences. Starting from core knowledge of signal and image processing, bioinformatics and computational biology, this track covers methods for biomedical image reconstruction, computational neuroengineering, well-being technologies and data protection and privacy.
• Pervasive Computing. Security and ubiquitous computing set the scene for this track which deals with data semantics, large scale software engineering, graph analytics and data protection and privacy.
• Visual Computing. This track lays the basics of signal & image processing and of computer graphics & augmented reality, and covers human computer interaction, computational vision, data visualisation, and computer games.

PROSPECTIVE CAREER

Senior expert in Data Science and Engineering. You will be at the forefront of the high-tech job market since all big companies are investing on data driven approaches for decision making and planning. The Business Intelligence area is highly regarded by consulting companies and large enterprises, while the Health and Life Sciences track is mainly oriented toward biomedical industry and research institutes. Both the Pervasive and the Visual Computing tracks are close to the interests of software companies. For all tracks a job in a start-up company or a career on your own are always in order.

Senior computer scientist.. By personalizing your plan of study you can keep open all the highly qualified job options in software companies.

Further graduate studies.. In all cases, you will be fully qualified to pursue your graduate studies toward a PhD in Computer Science.

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Our MSc Investigative Ophthalmology and Vision Research gives eye care professionals like you the academic knowledge and practical skills they need to contribute to groundbreaking medical advancements and effectively diagnose and treat a wide range of ocular conditions. Read more

Our MSc Investigative Ophthalmology and Vision Research gives eye care professionals like you the academic knowledge and practical skills they need to contribute to groundbreaking medical advancements and effectively diagnose and treat a wide range of ocular conditions.

Whether you want to pursue further academic studies or take on new responsibilities as a primary caregiver – our programme gives you the clinical skills and research experience you need. Advance your career and have a meaningful influence on the future of the field.

A combination of core and elective modules and research opportunities – with a research project making up one-third of the programme – allows you to customise your studies based on your personal interests and professional requirements. This includes the option to select a specialisation leading to a named degree on completion of your dissertation:

  • MSc Investigative Ophthalmology and Vision Research (General)
  • MSc Investigative Ophthalmology and Vision Research (Diabetes)
  • MSc Investigative Ophthalmology and Vision Research (Therapeutics)

What you will study

The programme is suited to applicants who have obtained a first degree in optometry or a related field outside of the UK. The generic stream of the programme gives a broader perspective than the diabetes and therapeutic routes

Modules

Advanced Binocular Vision; Advanced Clinical Investigation and Research Project; Chronic Complications of Diabetes; Clinical Ophthamology; Diabetes Care; Ocular Therapeutics; Practical and Theoretical Prescribing and Health Economics; Skills for Practice for Vision Science; Skills for Professional Practice Biosciences 2; and Public Health.

Students with an interest in diabetes can choose diabetes-related modules to enhance their understanding of how diabetes effects various structures of the human body. 

Core modules: Diabetes Care; Skills for Professional Practice for Vision Sciences; Skills for Professional Practice for Bioscience 2; Chronic Complications of Diabetes; Advanced Clinical Investigation and Research Project.

Optional modules: Clinical Ophthalmology; Health Economics and Public Health.

Graduate prospects

Whether you choose a path in academia or clinical practice, your MSc Investigative Ophthalmology and Vision Research will unlock new opportunities to advance and expand your career. The programme is ideal preparation for subsequent studies towards a PhD or other higher academic qualification. It also allows optometrists and other practitioners to advance their careers in primary eye care.



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Visit our website for more information on fees, scholarships, postgraduate loans and other funding options to study Computer Science. Read more

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

The MSc in Computer Science: Informatique is a Dual Degree scheme between Swansea University and Université Grenoble Alpes for computer science.

The MSc in Computer Science: Informatique Grenoble dual degree scheme is a two year programme that provides students with an opportunity to study in both Swansea, UK and Grenoble, France. One year of the Computer Science: Informatique programme students study at Swansea University and the second year of the programme students study at Université Grenoble Alpes. Upon successful completion of the programme, students will receive an M.Sc. in Advanced Computer Science from Swansea University and a Master from Université Grenoble Alpes.

Key Features of Computer Science: Informatique MSc

- We are top in the UK for career prospects [Guardian University Guide 2018]

- 5th in the UK overall [Guardian University Guide 2018]7th in the UK for student satisfaction with 98% [National Student Survey 2016]

- We are in the UK Top 10 for teaching quality [Times & Sunday Times University Guide 2017]

- 12th in the UK overall and Top in Wales [Times & Sunday Times University Guide 2017]

- 92% in graduate employment or further study six months after leaving University [HESA data 2014/15]

- UK TOP 20 for Research Excellence [Research Excellence Framework 2014]

- Our Project Fair allows students to present their work to local industry

- Strong links with industry

- £31m Computational Foundry for computer and mathematical sciences will provide the most up-to-date and high quality teaching facilities featuring world-leading experimental set-ups, devices and prototypes to accelerate innovation and ensure students will be ready for exciting and successful careers. (From September 2018)

- Top University in Wales [Times & Sunday Times University Guide 2017]

Modules of Computer Science: Informatique MSc

Modules on the MSc in Computer Science: Informatique may include:

Critical Systems; IT-Security: Theory and Practice; Visual Analytics; Data Science Research Methods and Seminars; Big Data and Data Mining; Data Visualization; Human Computer Interaction; Big Data and Machine Learning; Web Application Development; High Performance Computing in C/C++; Software Testing; Graphics Processor Programming; Embedded System Design; Mathematical Skills for Data Scientists; Logic in Computer Science; Computer Vision and Pattern Recognition; High-Performance Computing in C/C++; Hardware and Devices; 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, 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 our expansion, we are building the Computational Foundry on our Bay Campus for computer and mathematical sciences. This development is exciting news for Swansea Mathematics who are part of the vibrant and growing community of world-class research leaders drawn from computer and mathematical sciences.

Careers

All Computer Science courses will provide you the transferable skills and knowledge to help you take advantage of the excellent employment and career development prospects in an ever growing and changing computing and ICT industry.

94% of our Postgraduate Taught Computer Science Graduates were in professional level work or study [DLHE 14/15].

Some example job titles include:

Software Engineer: Motorola Solutions

Change Coordinator: Logica

Software Developer/Engineer: NS Technology

Workflow Developer: Irwin Mitchell

IT Developer: Crimsan Consultants

Consultant: Crimsan Consultants

Programmer: Evil Twin Artworks

Web Developer & Web Support: VSI Thinking

Software Developer: Wireless Innovations

Associate Business Application Analyst: CDC Software

Software Developer: OpenBet Technologies

Technical Support Consultant: Alterian

Programming: Rock It

Software Developer: BMJ Group

Research

The results of the Research Excellence Framework (REF) 2014 show that Swansea Computer Science ranked 11th in the UK for percentage of world-leading research, and 1st in Wales for research excellence. 40% of our submitted research assessed as world-leading quality (4*).



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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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Our Primary and Secondary PGCEs are "Outstanding" (Ofsted, 2015). All our Education courses have been developed in collaboration with Partnership schools and the National College for Teaching and Leadership (NCTL). Read more

About the course

Our Primary and Secondary PGCEs are "Outstanding" (Ofsted, 2015).

All our Education courses have been developed in collaboration with Partnership schools and the National College for Teaching and Leadership (NCTL). This ensures not only the highest possible quality of provision, but also relevance in reflecting national and school-level priorities in Education.

Aims

The Brunel Science Postgraduate Certificate (PGCE) is a M-level course with 60 credits that can contribute to further Master's level study in Education, subject to approval.

The course will equip you with the knowledge, understanding and skills necessary to teach science and the ability to:

Demonstrate an understanding of the vital role of the teacher and the school in ensuring excellence in the educational experiences of young people

Undertake professional practice which enables you to evidence the Teachers’ Standards which facilitate the award of Qualified Teacher Status

Understand the relationships between Education and science within current national and government frameworks, and critically reflect on the impact of these in the work of schools and the educational experiences of young people

Recognise the contribution that science as part of the whole school curriculum makes to the development of the individual learner and groups of learners

Think critically about what it means to be scientifically educated and how this informs curriculum planning and design within the subject area

Apply a thorough knowledge and understanding of science (Physics) National Curriculum to the planning of curriculum experiences for pupils in school

Demonstrate competence and confidence in your ability to teach across the contexts for pupil learning in the mathematics National Curriculum range and content, applying principles of continuity and progression

Use subject knowledge and relevant course specifications to plan and deliver the 14-16 curriculum including examination and vocational courses

Demonstrate an understanding of the subject knowledge and specification requirements for the 16-19 curriculum

Utilise a range of teaching strategies to meet the identified learning needs of a wide range of pupils

Utilise a range of resources, including information and communication technology, to enhance pupil learning in physics

Understand the importance of safe practice and safeguarding and apply these in working with young people both within and beyond lessons

Use a wide range of class management strategies to maximise pupil learning

Understand the principles of inclusion and apply these to ensure equality of opportunity for all pupils in the subject area

Understand national frameworks for assessment within the subject area and use these to support the recording and analysis of data, and the subsequent use of this to plan the next phase of learning

Raise the status of the subject area by demonstrating high standards of professionalism at all times

Understand the crucial role of professional learning for the teacher, the pupils and schools.

Course Content

The PGCE is an intensive programme, which combines an exploration of principles and methods of teaching and learning with practical school-based teaching placements. It lasts for 36 weeks from early September to late June.

The Secondary programme prepares you to work with pupils aged 11-16. At the heart of our programmes is a vision that our student teachers’ teaching will impact positively on pupil progress over time in schools and that our Partnership activities with schools will contribute to school improvement. We aspire for all our students to be outstanding teachers.

The PGCE Secondary courses are structured around three modules, which share a generic General Professional Education (GPE) component. The GPE programme involves an enquiry based learning approach, which combines taught sessions with independent professional learning activities (PLAs). These PLAs require independent research, which is either school-related or school-based. The three PGCE modules are:

1. Education Studies I
This module covers the following GPE themes:

Professionalism, values and reflective practice;
Safeguarding, child protection and e-safety;
Understanding curriculum and the National Curriculum;
Supporting learners, learning and effective behaviour management;
Inclusive education, with a specific focus on supporting pupils with SEND and SEBD;
Effective planning and teaching to promote pupil progress;
Assessment and its role in promoting effective learning.

You will also focus on teaching and learning issues of particular concern to your phase or subject specialism.

2. Education Studies II
This module covers the following GPE themes:

Applying for your first post;
Understanding data analysis to support effective teaching and learning;
Behaviour for learning and the wider professional responsibilities of the subject teacher;
Inclusive education, with a specific focus on supporting pupils with English as an Additional Language, pupils receiving the Pupil Premium and able pupils;
Safeguarding with a focus on the Prevent and Channel national strategy and bullying and homophobic bullying.

You will also continue to focus on teaching and learning issues of particular concern to your phase or subject specialism.

3. Education Studies III
This module focuses specifically on supporting student teachers to make an effective transition into their first post and examines the following themes in GPE:

Preparing for induction and the professional learning action plan for your first post;
Pathways into leadership in education;
Learning outside the classroom;
Contributing to the wider aspects of the formal and informal curriculum and your wider professional role as a teacher.

Subject Specific Course Content

As a qualified science teacher you may be required to teach National Curriculum general science to Key Stage 4, as well as your particular specialism to ‘A’ level and beyond. To this end, the course aims to facilitate your transformation into a well-educated, well-trained, confident and motivated science educator.

Along with English and mathematics, science is one of the three core subjects of the National Curriculum and since all pupils have to study a broad, balanced curriculum in science there is a demand for well-qualified and skilled science teachers. Most pupils entering secondary school are excited at the prospect of work, for the first time in a fully equipped laboratory, and secondary school science teachers have to build upon and sustain this interest for the subject.

To meet this challenge we need capable, skilled and enthusiastic teachers who are able to motivate young people and lead them to discover the wonders of science.

School Experience

School-based professional learning is a compulsory element of all programmes leading to a recommendation for QTS. The course involves the statutory requirement of at least 120 days of school experience in the form of block school placements undertaken in at least two different contexts.

Our current partnership schools are mainly located in the West London area and adjoining Home Counties. We have developed close links with a number of very good schools over a number of years, and offer placements within carefully chosen schools that provide an appropriate professional learning experience. The ethnic and cultural diversity of the schools we work with is a distinctive aspect of our provision and we are equally proud of the diversity of our student teacher cohort, who reflect the communities in which many of them go on to work as teachers.

We also offer student teachers the opportunity to experience placements in alternative settings, which include special schools, Pupil Referral Units (PRUs), young offenders institutions. This further demonstrates our commitment to preparing teachers to work with young people in a diverse range of educational contexts.

You will be allocated a school-based mentor, selected for their experience and expertise, who is there to help you develop and learn while you are on placement. The importance of this person should not be underestimated. Teaching is a very challenging profession and with the help of your school-based mentor and your University tutor we aim to make sure that you have support every step of the way, encouraging reflection and development.

Disclosure and Barring Service (DBS), Childcare Disqualification and Prohibition Orders

As an accredited provider of Initial Teacher Education we have to have regard to the Department for Education’s statutory guidance Keeping Children Safe in Education, when carrying out their duties to safeguard and promote the welfare of children. We ensure that all student teachers have been subject to Disclosure and Barring Service (DBS) criminal records checks, including a check of the children’s barred list. The Department for Education has published statutory guidance on the application to schools of the Childcare (Disqualification) Regulations 2009 and related obligations under the Childcare Act 2006.

We undertake our responsibility to ensure that the student teachers are not, therefore, disqualified from childcare or that the student teacher has obtained a childcare disqualification waiver from Ofsted. We also check that candidates are not subject to a prohibition order for teaching issued by the Secretary of State.

Teaching

We adopt an enquiry-based learning approach in our PGCE Secondary courses where students are encouraged to research and investigate a range of broad and subject specific educational themes and issues and bring their findings back for discussion in interactive lectures, workshops and seminars. These themes and issues address national, regional and partnership priorities as well as specific areas for investigation with the subject area.

Assessment

Postgraduate Certificate in Education (PGCE)
The PGCE Secondary programme carries 60 Master’s Level credits and requires you to successfully complete three formally assessed pieces of academic work during the year.
All of these assessments also require an accompanying portfolio of evidence.
The Master’s Level credits provide an excellent foundation for future academic and professional study.

Qualified Teacher Status (QTS)
Alongside the PGCE academic award for your programme, you will also be assessed for the recommendation of QTS. In order to be recommended for QTS you are required to demonstrate that you have met the Teachers’ Standards (DfE, 2013) in both the University and in school and alternative education settings. All aspects of the programme are designed around you being able to demonstrate that you are meeting the Teachers’ Standards.

Part 1 of the Teachers’ Standards require you to:

Set high expectations which inspire, motivate and challenge pupils
Promote good progress and outcomes by pupils
Demonstrate good subject and curriculum knowledge
Plan and teach well structured lessons
Adapt teaching to respond to the strengths and needs of all pupils
Make accurate and productive use of assessment
Manage behaviour effectively to ensure a good and safe learning environment
Fulfil wider professional responsibilities
(Teachers’ Standards, DfE, 2013)

Part 2 of the Teachers’ Standards require students to demonstrate the highest standards of personal and professional conduct.

As the PGCE is a professional course, 100% attendance is an expectation.

Recommendation for Qualified Teacher Status will be made by the Secondary PGCE Examination Board for all those who successfully demonstrate the Teachers’ Standards as shown in the requirements for University and school-based work.

Special Features

As a leading centre of education and with roots in teacher education dating back to 1798, we are able to provide first class teacher education that is internationally recognised.

A Brunel PGCE is a recognised symbol of quality teacher education which accounts for our high employment rates.

At the heart of our programmes is a vision that our student teachers’ teaching will impact positively on pupil progress over time in schools and that our partnership activities with schools will contribute to school improvement. We aspire for all our students to be outstanding teachers.

You will benefit from an established partnership between Brunel and a variety of educational institutions and local schools. Brunel education degrees offer multicultural placement learning opportunities. For example, our location in West London and our diverse and well-established schools network means you will gain highly-valued placement learning experiences in vibrant multicultural schools.

Beyond ITE, for early career teachers we offer the Masters in Teaching (MAT), where students can utilise their 60 PGCE Masters level credits to continue their postgraduate studies part-time, whilst also meeting the requirements outlined for Newly Qualified Teachers (NQTs) and early career development. Where schools have qualified for Enhanced Partnership status with Brunel University London, NQTs in those schools have access to the first year MAT module for free, illustrating our commitment to supporting NQTs into and through their first year of teaching. We also offer a Masters in Education (MAEd), a Doctorate in Education (EdDoc) and PhD postgraduate routes through the Department of Education. This continuum of provision ensures a commitment to teacher education and professional learning at all stages and the growing community of professional practice strengthens our Partnership.

Staff are nationally and internationally recognised for their research, and liaise with government and other agencies on education policy issues. The Department of Education is host to a number of research centres, including the Brunel Able Children’s Centre. The process of learning is informed by cutting-edge research by staff in the strands of: Science, Technology, Engineering and Mathematics (STEM) and Pedagogy and Professional Practice (PPP).

You can take advantage of free access to our excellent University Academic Skills service, ASK.

We have an award winning Professional Development Centre.

Our library has been nominated for national awards for its outstanding provision.

We have on-site volunteering opportunities through our Brunel Volunteers provision.

Our Disability and Dyslexia Service team have an excellent track record of support for students.

Our Union of Brunel Students provides you with a range of additional support and a broad range of extra-curricular opportunities and social events.

There is excellent University-wide access to PCs and the Internet, as well as free loan of media equipment and music/recording studios, and web space on the University server.

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See the department website - http://www.cis.rit.edu/graduate-programs/master-science. The master of science program in imaging science prepares students for positions in research in the imaging industry or in the application of various imaging modalities to problems in engineering and science. Read more
See the department website - http://www.cis.rit.edu/graduate-programs/master-science

The master of science program in imaging science prepares students for positions in research in the imaging industry or in the application of various imaging modalities to problems in engineering and science. Formal course work includes consideration of the physical properties of radiation-sensitive materials and processes, the applications of physical and geometrical optics to electro-optical systems, the mathematical evaluation of image forming systems, digital image processing, and the statistical characterization of noise and system performance. Technical electives may be selected from courses offered in imaging science, color science, engineering, computer science, science, and mathematics. Both thesis and project options are available. In general, full-time students are required to pursue the thesis option, with the project option targeted to part-time and online students who can demonstrate that they have sufficient practical experience through their professional activities.

Faculty within the Center for Imaging Science supervise thesis research in areas of the physical properties of radiation-sensitive materials and processes, digital image processing, remote sensing, nanoimaging, electro-optical instrumentation, vision, medical imaging, color imaging systems, and astronomical imaging. Interdisciplinary efforts are possible with other colleges across the university.

The program can be completed on a full- or a part-time basis. Some courses are available online, specifically in the areas of color science, remote sensing, medical imaging, and digital image processing.

Plan of study

All students must earn 30 credit hours as a graduate student. The curriculum is a combination of required core courses in imaging science, elective courses appropriate for the candidate’s background and interests, and either a research thesis or graduate paper/project. Students must enroll in either the research thesis or graduate paper/project option at the beginning of their studies.

Core courses

Students are required to complete the following core courses: Fourier Methods for Imaging (IMGS-616), Image Processing and Computer Vision (IMGS-682), Optics for Imaging (IMGS-633), and either Radiometry (IMGS-619) or The Human Visual System (IMGS-620).

Speciality track courses

Students choose two courses from a variety of tracks such as: digital image processing, medical imaging, electro-optical imaging systems, remote sensing, color imaging, optics, hard copy materials and processes, and nanoimaging. Tracks may be created for students interested in pursuing additional fields of study.

Research thesis option

The research thesis is based on experimental evidence obtained by the student in an appropriate field, as arranged between the student and their adviser. The minimum number of thesis credits required is four and may be fulfilled by experiments in the university’s laboratories. In some cases, the requirement may be fulfilled by work done in other laboratories or the student's place of employment, under the following conditions:

1. The results must be fully publishable.

2. The student’s adviser must be approved by the graduate program coordinator.

3. The thesis must be based on independent, original work, as it would be if the work were done in the university’s laboratories.

A student’s thesis committee is composed of a minimum of three people: the student’s adviser and two additional members who hold at least a master's dgeree in a field relevant to the student’s research. Two committee members must be from the graduate faculty of the center.

Graduate paper/project option

Students with demonstrated practical or research experience, approved by the graduate program coordinator, may choose the graduate project option (3 credit hours). This option takes the form of a systems project course. The graduate paper is normally performed during the final semester of study. Both part- and full-time students may choose this option, with the approval of the graduate program coordinator.

Admission requirements

To be considered for admission to the MS in imaging science, candidates must fulfill the following requirements:

- Hold a baccalaureate degree from an accredited institution (undergraduate studies should include the following: mathematics, through calculus and including differential equations; and a full year of calculus-based physics, including modern physics. It is assumed that students can write a common computer program),

- Submit a one- to two-page statement of educational objectives,

- Submit official transcripts (in English) of all previously completed undergraduate or graduate course work,

- Submit letters of recommendation from individuals familiar with the applicant’s academic or research capabilities,

- Submit scores from the Graduate Record Exam (GRE) (requirement may be waived for those not seeking funding from the Center for Imaging Science), and

- Complete a graduate application.

- International applicants whose native language is not English must submit scores from the Test of English as a Foreign Language. Minimum scores of 600 (paper-based) or 100 (Internet-based) are required. Students may also submit scores from the International English Language Testing System. The minimum IELTS score is 7.0. International students who are interested in applying for a teaching or research assistantship are advised to obtain as high a TOEFL or IELTS score as possible. These applicants also are encouraged to take the Test of Spoken English in order to be considered for financial assistance.

Applicants seeking financial assistance from the center must have all application documents submitted to the Office of Graduate Enrollment Services by January 15 for the next academic year.

Additional information

- Bridge courses

Applicants who lack adequate preparation may be required to complete bridge courses in mathematics or physics before matriculating with graduate status.

- Maximum time limit

University policy requires that graduate programs be completed within seven years of the student's initial registration for courses in the program. Bridge courses are excluded.

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The accredited Master of Science program in Computer Science is a two-year program that has been designed for international and German graduate students. Read more

The accredited Master of Science program in Computer Science is a two-year program that has been designed for international and German graduate students. The curriculum is very flexible. Students can compile their individual study plans based on their background and interests. It is also a very practical program. In addition to lectures and tutorials, students will complete two seminars, one or two projects and the master thesis.

In the beginning students will choose one or two key courses. Key courses are courses which introduce the students to the research areas represented at the Department of Computer Science. The following key courses are offered:

• Algorithm Theory

• Pattern Recognition

• Databases and Information Systems

• Software Engineering

• Artificial Intelligence

• Computer Architecture

After that, students can specialize in one of the following three areas:

• Cyber-Physical Systems

• Information Systems

• Cognitive Technical Systems

Here are some examples of subjects offered in the three specialization areas:

Cyber-Physical Systems:

• Cyber-Physical Systems – Discrete Models

• Cyber-Physical Systems – Hybrid Control

• Real Time Operation Systems and Reliability

• Verification of Embedded Systems

• Test and Reliability

• Decision Procedures

• Software Design, Modeling and Analysis in UML

• Formal Methods for Java

• Concurrency: Theory and Practice

• Compiler Construction

• Distributed Systems

• Constraint Satisfaction Problems

• Modal Logic

• Peer-to-Peer Networks

• Program Analysis

• Model Driven Engineering

Information Systems:

• Information Retrieval Data Models and Query Languages

• Peer-to-Peer Networks

• Distributed Storage

• Software Design, Modeling and Analysis in UML

• Security in Large-Scale Distributed Enterprises

• Machine Learning

• Efficient Route Planning

• Bioinformatics I

• Bioinformatics II

• Game Theory

• Knowledge Representation

• Distributed Systems

Cognitive Technical Systems:

• Computer Vision I

• Computer Vision II

• Statistical Pattern Recognition

• Mobile Robotics II

• Simulation in Computer Graphics

• Advanced Computer Graphics

• AI Planning

• Game Theory

• Knowledge Representation

• Constraint Satisfaction Problems

• Modal Logic

• Reinforcement Learning

• Machine Learning

• Mobile Robotics I

We believe that it is important for computer science students to get a basic knowledge in a field in which they might work after graduation. Therefore, our students have the opportunity to complete several courses and/or a project in one of the following application areas:

  • Bioinformatics
  • Microsystems Engineering
  • Neuroscience
  • Economics

In the last semester, students work on their master’s thesis. They are expected to tackle an actual research question in close cooperation with a professor and his/her staff.



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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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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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Computer vision and imaging is the exciting science and technology of machines that see, concerned with building artificial systems that obtain information from images that are derived from a range of sources. Read more
Computer vision and imaging is the exciting science and technology of machines that see, concerned with building artificial systems that obtain information from images that are derived from a range of sources. This MSc in Computing with Vision and Imaging teaches you the skills necessary to undertake work in this ever-evolving field.

Why study at Dundee?

Computer vision and imaging is a rapidly expanding field with plenty of real-life applications and opportunities. Here at Dundee, we encourage a professional, inter-disciplinary and user-centred approach to computer systems design and production.

Application areas include:
controlling processes - e.g. an industrial robot or an autonomous vehicle
detecting events - e.g. for visual surveillance or people counting
organising information - e.g. for indexing databases of images and image sequences
modelling objects or environments - e.g. for industrial inspection
medical image analysis
topographical modelling

You will acquire skills in computer vision, inference, algorithmic underpinnings of computer vision systems, how images and signals are formed, filter, compressed and analysed, and how multiple images can be combined.

Throughout this course, you will also develop the necessary skills to undertake independent research and participate in proposal development and innovation - an excellent grounding for many future careers.

What's Great about studying at Dundee?

Research-led teaching:
Teaching at Dundee is research-led, meaning that the MSc programme benefits from association with cutting-edge research of international standard and its commercial applications.

We also have an active Computer Vision and Image Processing research group. Our Vision and Imaging students are involved in a number of http://www.computing.dundee.ac.uk/projects/vision/projects.php, and have been involved with a number of completed research projects like ACTIVE, a project concerning adaptive interfaces for the operation of secondary controls in motor vehicles using pointing gestures and virtual dashboards.

Links with industry

The School of Computing collaborates with, and has links to, companies such as IBM, NCR and Oracle.

Our facilities

You will have 24-hour access to our award winning and purpose-built Queen Mother Building. It has an unusual mixture of lab space and breakout areas, with a range of conventional and special equipment for you to use. It's also easy to work on your own laptop as there is wireless access throughout the building. Our close ties to industry allows us access to facilities such as Windows Azure and Teradata, and university and industry standard software such as Tableau for you to evaluate and use.

Postgraduate culture

The School of Computing maintains a friendly, intimate and supportive atmosphere, and we take pride in the fact that we know all of our students - you're far more than just a matriculation number to us. We have a thriving postgraduate department with regular seminars and guest speakers.

What you will study

You select seven taught modules, three per semester, during the period September-April. You will make module selections with your advisor.

Semester 1 (Sept-Dec):
Probabilistic Inference and Learning
Signals and Images

Plus two from:
Technology Innovation Management
Computer Graphics
Logical Inference & Symbolic Reasoning
Information Theory

Semester 2 (Jan-Mar):
Vision and Perception
Research Methods

Plus one from:
Computing Research Frontiers
Multi-agent Systems & Grid Computing

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.

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.

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

Careers

The knowledge, skills and understanding that you will gain in the areas of computer vision, inference and learning will enable you to work effectively in the application of video and image-based computing - whether you choose industry, commerce or research.

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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The Master of Science in Exercise and Nutrition Science prepares students to work in government, business, the sports industry and in education as practitioners on professional interdisciplinary teams. Read more
The Master of Science in Exercise and Nutrition Science prepares students to work in government, business, the sports industry and in education as practitioners on professional interdisciplinary teams. The program is for students seeking a terminal degree as well as for those seeking a strong foundation for further study and research. The program offers three entry points throughout the academic year, and courses are scheduled to allow an efficient timeline to degree completion for full-time students. Students are provided experiential learning opportunities both inside and outside of the classroom, and are prepared for both the Certified Sports Nutritionist (CISSN) and Certified Strength and Conditioning Specialist (CSCS) examinations, the premier certifications in strength and conditioning and sports nutrition, upon graduation if they choose to pursue certification.

Visit the website http://www.ut.edu/msexercisenutrition/

High-Tech Facilities

Having published more than 100 papers and abstracts, and secured several hundred thousand dollars in funding over the last three years, the students and staff working in the UT Human Performance Research Lab have become nationally and internationally recognized. The lab is one of the most sophisticated and advanced human performance and sport nutrition laboratories in the world, allowing students the opportunity to advance their skills in human performance testing. Equipment contained in the lab includes:

- AMTI force plate for power and velocity

- Dynavision for vision training, reaction time and cognitive function

- Tendo units for movement, specifically power and velocity

- Ultrasonography to measure skeletal muscle size, locate soft tissue injuries and quantify blood flow and blood vessel diameter

- Wingate peak power bikes for anaerobic power testing

- Electromyography for neural function and skeletal muscle activation

- Metabolic carts for VO2 max and resting metabolism measures

- Dual X-ray absorbtiometry for bone mineral density, lean mass and fat mass

- Minus 80°C freezer to maintain the integrity of biological samples

- High tech motion analysis and heavy duty motorized treadmills with 40-degree incline ability

- BTR Primus isokinetic, isotonic and isometric dynomometers for measurement of force, power and velocity in virtually any plane

- Blood lactate analyzers to examine metabolic stress and lactate threshold

- A fully equiped strength and conditioning laboratory

Converging Exercise and Nutrition Sciences Like Never Before

Most university programs segregate the study of exercise and nutrition sciences. The goal of UT’s M.S. in Exercise and Nutrition Science is to examine the relationship between the two fields in regard to optimizing athletic performance. The program combines advanced concepts from exercise physiology and strength and conditioning to teach students how nutrition can impact each area. Through numerous hands-on experiences and rigorous classroom study, students gain an unparalleled awareness of the intersection of these sciences.

Learning by Doing

M.S.-ENS students “learn by doing” through performance-based programming, which prepares practitioners to work with a wide variety of athletes. The department’s advanced labs and technology help students prepare for the real world. UT’s relationships with numerous local athletic teams such as the Tampa Bay Buccaneers and Tampa Bay Lightning allow students put their theories to test. UT faculty and students have also conducted extensive research with more than a dozen high-impact companies that are involved in exercise and nutrition/supplementation. These collaborations give students an insider’s view of the industry and provide a strong network for post-graduation jobs.

Internationally Recognized

Based on the rigor and innovation of the M.S.-ENS program, the International Society of Sports Nutrition recognized it as the first graduate program in Florida to offer approved coursework for preparation for the CISSN examination.

Outstanding Faculty

The program’s highly respected faculty has achieved national and international reputations for academic and applied success in their respective fields.

- J.C. Andersen, Ph.D. – pain and sports medicine

- Mary Martinasek, Ph.D. – mixed-method research inquiry and health program evaluation

- Jay O’Sullivan, Ph.D. – internships in exercise and nutrition science

- Ronda Sturgill, Ph.D. – kinesiology and program evaluation

- Eric Vlahov, Ph.D. – exercise physiology, nutrition and sports psychology

Flexible Program

Our highly flexible program allows students to complete the program within one year. With three entry points into the program, students are able to take classes throughout the year and take time off as needed.

Find out how to apply here - http://www.ut.edu/apply

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