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Masters Degrees (Affective Computing)

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Affective computing is an exciting, multi-disciplinary strand of computing that addresses how computers, and other technologies, will become more interactive and efficient by recognising, and responding to, human emotions. Read more

Affective computing is an exciting, multi-disciplinary strand of computing that addresses how computers, and other technologies, will become more interactive and efficient by recognising, and responding to, human emotions.

This course offers students a unique opportunity to be at the forefront of intelligent, emotionally interactive technologies as they come to fruition in the industry and marketplace over the next 10 years. Utilising emergent technologies, such as the Internet of Things (IoT), wearable and mobile devices, and Big Data, the course combines theory and practice, as it prepares students to seize the opportunity to create innovative computers that are powerful, customisable, adaptive, and responsive to their users.

Ultimately, affective computing can provide a way for humans to seamlessly filter out a lot of the information they are presently swamped with and to get to the services and systems that are right for them. 

Indications from the Tech Partnership skills council show that there is a need for 1 million new employment roles in the digital economy between now and 2025 and that 52% of digital businesses currently struggle to fill specialist vacancies.

Key course features

  • Gain hands-on experience of working with a range of sensors and equipment in building experimental, affective computing systems.
  • Learn about the emerging fields of Affective Computing, the Internet of Things (IoT), and Big Data.
  • The course is taught and assessed by active researchers in the field, who all belong to the University’s Affective Audio and/or ARClab groups.
  • The ability to critically appraise and disseminate research results.
  • Provides students with a sound basis for further research and/or professional development.

What will you study

The course provides students with immersion in several distinct subject disciplines that support the design, development, and evaluation of affective computing systems. The course modules cover the practical skills of computing, necessary to build affective, interactive technologies, supported by learning the theories, investigation techniques, and research skills that allow them to work successfully with leading edge, emerging technologies and devise solutions that are fit for purpose.

 

ALL MODULES ARE CORE.

 

As with most masters programmes this has 2 parts, a taught part followed by a dissertation.

 

Students study 5 core modules, totalling 120 credits, followed by a 60 credit dissertation, making a total of 180 credits.

 

MODULES:

  • Affective Computing: This module introduces students to the theory and practical application of affective computing. Students will gain insight into the multi-disciplinary aspects and influences of affective computing and the various models and paradigms of emotion. Students will learn to design, construct, and test affective systems to address specific problems. As such, students will gain experience in configuring a range of sensors, and interpreting the data they produce, in a hands-on fashion.
  • Human Factors Engineering: This module provides a range of skills that can be applied in the development efficient technologies that are easy to use and highly effective. As such, the module provides students with a deep knowledge of the societal, psychological, physical, and technical factors relating to human factors engineering. Students will develop a degree of expertise in human factors engineering, particularly focused on the evaluation of existing information and computer systems. In practical terms, students will conduct and report upon usability studies in a mature and professional manner, with an awareness of the legal and ethical issues involved.
  • Advanced Artificial Intelligence: In this module students are given the opportunity to study problem-solving techniques that are applicable to artificial intelligence with the intention of providing them with the ability to develop intelligent systems. Investigating the role of human intelligence from the Computer Science point of view will enable students to appreciate the role of problem solving. Typical techniques include identification trees, neural nets, genetic algorithms, sparse spaces, near misses particularly applicable to nearest neighbours will be studied. These techniques will enable students to tackle problems in the areas of machine learning, pattern recognition, natural language processing and understanding, perception and expert systems.
  • Postgraduate Study and Research Methods: This module will provide the necessary underpinning skills to ensure that competent work and standards are achieved and maintained throughout the student’s chosen programme of study. This will encompass the development of professional level information handling and analysis skills, as well as ensuring students become proficient at recognising and managing their own professional development.
  • Future and Emerging Technologies: The module explores emerging and future technologies in the field of computing and affords students the opportunity to investigate novel application and research areas and environments where computing can be potentially beneficial. Consideration is given to the the legal, ethical, social, political, economic, environmental, demographic, philosophical and cultural issues on which future technologies may have influence, and be influenced by. Students are expected to apply research methods and forecasting techniques to make and justify credible predictions in their field of study.
  • Dissertation: The dissertation is a study-led piece of work that focuses on applying a wide range of the technical and critical analysis skills that have been developed throughout the course. Students will agree a topic of study with their academic supervisor that falls within the remit of affective computing. This typically follows the development and implementation of a computer system and or may be based upon a research investigation.

For a full-time student, the taught components (all modules apart from the Dissertation) of the course, requiring attendance in a classroom or lab, will be in the region of 12 hours per week during each semester. In addition, students are expected to study independently outside of the classroom for around 15 hours per week. The commitment for a part-time student is approximately half that of a full-time student.

The information listed in this section is an overview of the academic content of the programme that will take the form of either core or option modules. Modules are designated as core or option in accordance with professional body requirements and internal academic framework review, so may be subject to change.





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The School conducts high-quality significant national and international research and offers excellent opportunities for graduate studies, successfully combining modern engineering and technology with the exciting field of digital media. Read more
The School conducts high-quality significant national and international research and offers excellent opportunities for graduate studies, successfully combining modern engineering and technology with the exciting field of digital media. The digital media group has interests in many areas of interactive multimedia and digital film and animation.

Visit the website https://www.kent.ac.uk/courses/postgraduate/264/digital-arts

About the School of Engineering and Digital Arts

Established over 40 years ago, the School has developed a top-quality teaching and research base, receiving excellent ratings in both research and teaching assessments.

The School undertakes high-quality research (http://www.eda.kent.ac.uk/research/default.aspx) that has had significant national and international impact, and our spread of expertise allows us to respond rapidly to new developments. Our 30 academic staff and over 130 postgraduate students and research staff provide an ideal focus to effectively support a high level of research activity. There is a thriving student population studying for postgraduate degrees in a friendly and supportive teaching and research environment.

We have research funding from the Research Councils UK, European research programmes, a number of industrial and commercial companies and government agencies including the Ministry of Defence. Our Electronic Systems Design Centre and Digital Media Hub provide training and consultancy for a wide range of companies. Many of our research projects are collaborative, and we have well-developed links with institutions worldwide.

Course structure

The digital media group has interests in many areas of interactive multimedia and digital film and animation.

There is particular strength in web design and development, including e-commerce, e-learning, e-health; and the group has substantial experience in interaction design (eg, Usability and accessibility), social computing (eg, Social networking, computer mediated communication), mobile technology (eg, iPhone), virtual worlds (eg, Second Life) and video games. In the area of time-based media, the group has substantial interest in digital film capture and editing, and manipulation on to fully animated 3D modelling techniques as used in games and feature films.

Research Themes:
- E-Learning Technology (http://www.eda.kent.ac.uk/research/theme_detail.aspx?gid=1&tid=1)

- Medical Multimedia Applications and Telemedicine (http://www.eda.kent.ac.uk/research/theme_detail.aspx?gid=1&tid=2)

- Human Computer Interaction and Social Computing (http://www.eda.kent.ac.uk/research/theme_detail.aspx?gid=1&tid=3)

- Computer Animation and Digital Visual Effects (http://www.eda.kent.ac.uk/research/theme_detail.aspx?gid=1&tid=4)

- Mobile Application Design and Development (http://www.eda.kent.ac.uk/research/theme_detail.aspx?gid=1&tid=25)

- Digital Arts (http://www.eda.kent.ac.uk/research/theme_detail.aspx?gid=1&tid=26)

Research areas

- Intelligent Interactions

The Intelligent Interactions group has interests in all aspects of information engineering and human-machine interactions. It was formed in 2014 by the merger of the Image and Information Research Group and the Digital Media Research Group.

The group has an international reputation for its work in a number of key application areas. These include: image processing and vision, pattern recognition, interaction design, social, ubiquitous and mobile computing with a range of applications in security and biometrics, healthcare, e-learning, computer games, digital film and animation.

- Social and Affective Computing
- Assistive Robotics and Human-Robot Interaction
- Brain-Computer Interfaces
- Mobile, Ubiquitous and Pervasive Computing
- Sensor Networks and Data Analytics
- Biometric and Forensic Technologies
- Behaviour Models for Security
- Distributed Systems Security (Cloud Computing, Internet of Things)
- Advanced Pattern Recognition (medical imaging, document and handwriting recognition, animal biometrics)
- Computer Animation, Game Design and Game Technologies
- Virtual and Augmented Reality
- Digital Arts, Virtual Narratives.

Careers

We have developed our programmes with a number of industrial organisations, which means that successful students are in a strong position to build a long-term career in this important discipline. You develop the skills and capabilities that employers are looking for, including problem solving, independent thought, report-writing, time management, leadership skills, team-working and good communication.

Kent has an excellent record for postgraduate employment: over 94% of our postgraduate students who graduated in 2013 found a job or further study opportunity within six months.

Building on Kent’s success as the region’s leading institution for student employability, we offer many opportunities for you to gain worthwhile experience and develop the specific skills and aptitudes that employers value.

Find out how to apply here - https://www.kent.ac.uk/courses/postgraduate/apply/

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The Computer Animation Master’s programme at Kent is oriented towards current industrial needs, technology and practice. It is designed to be a direct route into this high-profile, modern and creative industry, and has been developed jointly by the School and our industrial partner Framestore CFC. Read more
The Computer Animation Master’s programme at Kent is oriented towards current industrial needs, technology and practice. It is designed to be a direct route into this high-profile, modern and creative industry, and has been developed jointly by the School and our industrial partner Framestore CFC.

Develop your knowledge and understanding of the animation process, software tools, techniques and packages, and the technical aspects of working in a professional animation environment. The MSc programme offers invaluable experience of working to professional briefs and under expert supervision of professional animators to prepare you for a career in industry.

Competition is fierce in animation and visual effects and success depends on your concentration levels, constant practise and ability to grasp the essence and modern techniques of animation. Successful former students are now working in animation and animation layout roles for companies such as Sony Games and Framestore CFC on major titles in games, television and film.

Visit the website https://www.kent.ac.uk/courses/postgraduate/248/computer-animation

About the School of Engineering and Digital Arts

The School of Engineering and Digital Arts successfully combines modern engineering and technology with the exciting field of digital media. The School, which was established over 40 years ago, has developed a top-quality teaching and research base, receiving excellent ratings in both research and teaching assessments.

The School undertakes high-quality research that has had significant national and international impact, and our spread of expertise allows us to respond rapidly to new developments. Our 30 academic staff and over 130 postgraduate students and research staff provide an ideal focus to effectively support a high level of research activity. We have a thriving student population studying for postgraduate degrees in a friendly, supportive teaching and research environment.

We have research funding from the Research Councils UK, European research programmes, a number of industrial and commercial companies and government agencies including the Ministry of Defence. Our Electronic Systems Design Centre and Digital Media Hub provide training and consultancy for a wide range of companies. Many of our research projects are collaborative, and we have well-developed links with institutions worldwide.

Course structure

This intensively taught postgraduate course lasts a full year. It takes place in a dedicated computer laboratory where you have your own seat and computer for the duration of the course. The course lectures and workshops, whether led by visiting professionals or staff, are all held in this room. Demonstrations and showing of films are by means of an HD projector. By the end of the year, the lab will be where you live as much as your accommodation.

Modules

The following modules are indicative of those offered on this programme. This list is based on the current curriculum and may change year to year in response to new curriculum developments and innovation. Most programmes will require you to study a combination of compulsory and optional modules. You may also have the option to take modules from other programmes so that you may customise your programme and explore other subject areas that interest you.

EL831 - Digital Visual Art set-up (15 credits)
EL832 - Animation Principles (15 credits)
EL833 - Visual Training (15 credits)
EL837 - Professional Group Work (15 credits)
EL863 - Advanced 3D Modelling (15 credits)
EL864 - Pre-Visualisation (15 credits)
EL865 - Action in Animation (15 credits)
EL866 - Acting in Animation (15 credits)
EL830 - Computer Animation Project (60 credits)

Assessment

Each module is assessed by practical assignments. The project work is assessed on the outcome of the project itself.

Programme aims

This programme aims to:

- enable you to develop your knowledge and understanding within the field of 3D computer animation, which will equip you to become a professional in the animation and visual effects industry

- produce professionally-trained animators who are highly skilled in using state-of-the-art 3D animation software for producing animated films

- provide you with proper academic guidance and welfare support

- create an atmosphere of co-operation and partnership between staff and students, and offer you an environment where you can develop your potential

- strengthen and expand opportunities for industrial collaboration with the School of Engineering and Digital Arts.

Research areas

- Intelligent Interactions

The Intelligent Interactions group has interests in all aspects of information engineering and human-machine interactions. It was formed in 2014 by the merger of the Image and Information Research Group and the Digital Media Research Group.

The group has an international reputation for its work in a number of key application areas. These include: image processing and vision, pattern recognition, interaction design, social, ubiquitous and mobile computing with a range of applications in security and biometrics, healthcare, e-learning, computer games, digital film and animation.

- Social and Affective Computing
- Assistive Robotics and Human-Robot Interaction
- Brain-Computer Interfaces
- Mobile, Ubiquitous and Pervasive Computing
- Sensor Networks and Data Analytics
- Biometric and Forensic Technologies
- Behaviour Models for Security
- Distributed Systems Security (Cloud Computing, Internet of Things)
- Advanced Pattern Recognition (medical imaging, document and handwriting recognition, animal biometrics)
- Computer Animation, Game Design and Game Technologies
- Virtual and Augmented Reality
- Digital Arts, Virtual Narratives.

Careers

We have developed the programme with a number of industrial organisations, which means that successful students will be in a strong position to build a long-term career in this important discipline.

The School of Engineering and Digital Arts (http://www.eda.kent.ac.uk/) has an excellent record of student employability (http://www.eda.kent.ac.uk/school/employability.aspx). We are committed to enhancing the employability of all our students, to equip you with the skills and knowledge to succeed in a competitive, fast-moving, knowledge-based economy.

Graduates who can show that they have developed transferable skills and valuable experience are better prepared to start their careers and are more attractive to potential employers.

Find out how to apply here - https://www.kent.ac.uk/courses/postgraduate/apply/

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The School of Engineering and Digital Arts offers research-led degrees in a wide range of research disciplines, related to Electronic, Control and Information Engineering, in a highly stimulating academic environment. Read more
The School of Engineering and Digital Arts offers research-led degrees in a wide range of research disciplines, related to Electronic, Control and Information Engineering, in a highly stimulating academic environment. The School enjoys an international reputation for its work and prides itself in allowing students the freedom to realise their maximum potential.

Established over 40 years ago, the School has developed a top-quality teaching and research base, receiving excellent ratings in both research and teaching assessments.

We undertake high-quality research that has had significant national and international impact, and our spread of expertise allows us to respond rapidly to new developments. Our 30 academic staff and over 130 postgraduate students and research staff provide an ideal focus to effectively support a high level of research activity. There is a thriving student population studying for postgraduate degrees in a friendly and supportive teaching and research environment.

We have research funding from the Research Councils UK, European research programmes, a number of industrial and commercial companies and government agencies including the Ministry of Defence. Our Electronic Systems Design Centre and Digital Media Hub provide training and consultancy for a wide range of companies. Many of our research projects are collaborative, and we have well-developed links with institutions worldwide.

Visit the website https://www.kent.ac.uk/courses/postgraduate/262/electronic-engineering

Project opportunities

Some projects available for postgraduate research degrees (http://www.eda.kent.ac.uk/postgraduate/projects_funding/pgr_projects.aspx).

Research areas

- Communications

The Group’s activities cover system and component technologies from microwave to terahertz frequencies. These include photonics, antennae and wireless components for a broad range of communication systems. The Group has extensive software research tools together with antenna anechoic chambers, network and spectrum analysers to millimetre wave frequencies and optical signal generation, processing and measurement facilities. Current research themes include:

- photonic components
- networks/wireless systems
- microwave and millimetre-wave systems
- antenna systems
- radio-over-fibre systems
- electromagnetic bandgaps and metamaterials
- frequency selective surfaces.

- Intelligent Interactions:

The Intelligent Interactions group has interests in all aspects of information engineering and human-machine interactions. It was formed in 2014 by the merger of the Image and Information Research Group and the Digital Media Research Group.

The group has an international reputation for its work in a number of key application areas. These include: image processing and vision, pattern recognition, interaction design, social, ubiquitous and mobile computing with a range of applications in security and biometrics, healthcare, e-learning, computer games, digital film and animation.

- Social and Affective Computing
- Assistive Robotics and Human-Robot Interaction
- Brain-Computer Interfaces
- Mobile, Ubiquitous and Pervasive Computing
- Sensor Networks and Data Analytics
- Biometric and Forensic Technologies
- Behaviour Models for Security
- Distributed Systems Security (Cloud Computing, Internet of Things)
- Advanced Pattern Recognition (medical imaging, document and handwriting recognition, animal biometrics)
- Computer Animation, Game Design and Game Technologies
- Virtual and Augmented Reality
- Digital Arts, Virtual Narratives.

- Instrumentation, Control and Embedded Systems:

The Instrumentation, Control and Embedded Systems Research Group comprises a mixture of highly experienced, young and vibrant academics working in three complementary research themes – embedded systems, instrumentation and control. The Group has established a major reputation in recent years for solving challenging scientific and technical problems across a range of industrial sectors, and has strong links with many European countries through EU-funded research programmes. The Group also has a history of industrial collaboration in the UK through Knowledge Transfer Partnerships.

The Group’s main expertise lies primarily in image processing, signal processing, embedded systems, optical sensors, neural networks, and systems on chip and advanced control. It is currently working in the following areas:

- monitoring and characterisation of combustion flames
- flow measurement of particulate solids
- medical instrumentation
- control of autonomous vehicles
- control of time-delay systems
- high-speed architectures for real-time image processing
- novel signal processing architectures based on logarithmic arithmetic.

Careers

We have developed our programmes with a number of industrial organisations, which means that successful students are in a strong position to build a long-term career in this important discipline. You develop the skills and capabilities that employers are looking for, including problem solving, independent thought, report-writing, time management, leadership skills, team-working and good communication.

Kent has an excellent record for postgraduate employment: over 94% of our postgraduate students who graduated in 2013 found a job or further study opportunity within six months.

Building on Kent’s success as the region’s leading institution for student employability, we offer many opportunities for you to gain worthwhile experience and develop the specific skills and aptitudes that employers value.

Find out how to apply here - https://www.kent.ac.uk/courses/postgraduate/apply/

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The MSc covers web-related technologies and big data analytics. It is intended for students with a general science and engineering background and is an ideal preparation for roles with some of the best internet-related industries and areas requiring big data analytical skills. Read more

The MSc covers web-related technologies and big data analytics. It is intended for students with a general science and engineering background and is an ideal preparation for roles with some of the best internet-related industries and areas requiring big data analytical skills.

About this degree

Students will gain a detailed knowledge and understanding of web-related technologies and big data analytics, ranging from information search and retrieval, natural language processing, data mining and knowledge acquisition, large-scale distributed data analytics and cloud computing to e-commerce and their business economic models and the latest concepts of social networks.

MSc students undertake modules to the value of 180 credits.

The programme consists of three core modules (45 credits), five optional modules (75 credits), and the research dissertation (60 credits).

Core modules

  • Complex Networks and Web (15 credits)
  • Information Retrieval and Data Mining (15 credits)
  • Web Economics (15 credits)

Optional modules

Students must choose a minimum of 45 and a maximum of 75 credits of optional modules. Up to two electives (30 credits) may also be chosen instead of two of the optional modules.

  • Affective Computing and Human-Robot Interaction (15 credits)
  • Applied Machine Learning (15 credits)
  • Birkbeck College: Cloud Computing (15 credits)
  • Computer Graphics (15 credits)
  • Entrepreneurship: Theory and Practice (15 credits)
  • Graphical Models (15 credits)
  • Interaction Design (15 credits)
  • Machine Vision (15 credits)
  • Probabilistic and Unsupervised Learning (15 credits)
  • Statistical Natural Language Processing (15 credits)
  • Supervised Learning (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 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 help from demonstrators. Student performance is assessed by unseen written examinations, coursework and the dissertation.

Careers

Graduates from UCL are keenly sought after by the world's leading organisations, and many progress in their careers to secure senior and influential positions. Graduates of our Web Science and Big Data Analytics programmes are expected to develop careers in scientific research, the internet-based industry and other professional areas that require big data analytics skills.

Recent career destinations for this degree

  • CEO (Chief Executive Officer), Hoxton Analytics
  • Software Engineer, China Mobile
  • Computer Science Lecturer, Singapore Polytechnic
  • Software Developer, Barclays
  • Software Engineer, UCL

Employability

The MSc has a unique industry connection as almost all our lecturers have industry experience. Through long-term collaborations with big players in the field such as Google, Microsoft and BT, research is driven by the fundamental technical challenges faced by the industry. Throughout the degree, our students have the chance to interact with our industry collaborators and previous students, and have placement opportunities to address the specific technical problems faced by the industry.

Why study this degree at UCL?

UCL Computer Science is recognised as a world leader in teaching and research.

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 Master's programmes have some of the highest employment rates and starting salaries, with graduates entering a wide variety of industries, from entertainment to finance.

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.



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Understanding all aspects of Human-Robot interaction. the programming that coordinates a robot’s actions with human action as well the human appreciation and trust in the robot. Read more

Understanding all aspects of Human-Robot interaction: the programming that coordinates a robot’s actions with human action as well the human appreciation and trust in the robot.

At present, there are many sensors and actuators in every device – so they may become embedded in a physical reality. For robots that move around in a specific setting there is a pressing need for the development of proper methods of control and joint-action. The embedded, embodied nature of human cognition is an inspiration for this, and vice versa. Computational modelling of such tasks can give insight into the nature of human mental processing. In the Master’s specialisation in Robot Cognition you’ll learn all about the sensors, actuators and the computational modelling that connects them.

Making sense of sensor data – developing artificial perception – is no trivial task. The perception, recognition and even appreciation of sound stimuli for speech and music (i.e. auditory scene analysis) require modelling and representation at many levels and the same holds for visual object recognition and computer vision. In this area, vocal and facial expression recognition (recognition of emotion from voices and faces) is a rapidly growing application area. In the area of action and motor planning, sensorimotor integration and action, there are strong links with research at the world-renowned Donders Centre for Cognition.

At Radboud University we also look beyond the technical side of creating robots that can move, talk and interpret emotions as humans do. We believe that a robot needs to do more than simply function to its best ability. A robot that humans distrust will fail even if it is well programmed. Culture also plays a role in this; people in Japan are more open to the possibilities of robots than in, for example, the Netherlands. We will teach you how to evaluate humans’ attitudes towards a robot in order to use that information to create robots that will be accepted and trusted and therefore perform even better.

See the website http://www.ru.nl/masters/ai/robot

Why study Robot Cognition at Radboud University?

- We offer a great mix of technical and social aspects of robot cognition.

- This programme focuses on programming robot behaviours and evaluating them rather than building the robots themselves. We teach you to programme robots that will be used in close contact with human beings, for example in healthcare and education, rather than in industry.

- Our cognitive focus leads to a highly interdisciplinary AI programme where students gain skills and knowledge from a number of different areas such as mathematics, computer science, psychology and neuroscience combined with a core foundation of artificial intelligence.

- This specialisation offers plenty of room to create a programme that meets your own academic and professional interests.

- Together with the world-renowned Donders Institute, the Max Planck Institute and various other leading research centres in Nijmegen, we train our students to become excellent researchers in AI.

- To help you decide on a research topic there is a semi-annual Thesis Fair where academics and companies present possible project ideas. Often there are more project proposals than students to accept them, giving you ample choice. We are also open to any of you own ideas for research.

- Our AI students are a close-knit group; they have their own room in which they often get together to interact, debate and develop their ideas. Every student also receives personal guidance and supervision from a member of our expert staff.

Our research in this field

The programme is closely related to the research carried out in the internationally renowned Donders Institute for Brain, Cognition and Behaviour. This institute has several unique facilities for brain imaging using EEG, fMRI and MEG. You could also cooperate with the Behavioural Science Institute and work in its Virtual Reality Laboratory, which can be used to study social interaction between humans and avatars.

An example of a possible thesis subject:

- Engaging human-robot interactions in healthcare for children and/or the elderly

Social robots are often deployed with 'special' user groups such as children and elderly people. Developing and evaluating robot behaviours for these user groups is a challenge as a proper understanding of their cognitive and social abilities is needed. Depending on the task, children for example need to be engaged and encouraged in a different way than adults do. What are effective robot behaviours and strategies to engage children and/or elderly people? How can these robot behaviours be evaluated in a proper way?

Career prospects

Our Artificial Intelligence graduates have excellent job prospects and are often offered a job before they have actually graduated. Many of our graduates go on to do a PhD either at a major research institute or university with an AI department. Other graduates work for companies interested in cognitive design and research. Examples of companies looking for AI experts with this specialisation: Philips, Siemens, Honda, Mercedes, Google. Some students have even gone on to start their own companies.

Job positions

Examples of jobs that a graduate of the specialisation in Robot Cognition could get:

- PhD Researcher on Cognitive-Affective Modelling for Social Robots

- PhD Researcher on Automatic analysis of human group behaviour in the presence of robots

- PhD Researcher on Automatic analysis of affective quality of conversations in human-robot interaction

- Advisor and innovation manager in the healthcare industry

- Social robotics and affective computing for robots expressing emotions

- Developer of control algorithms for using optic flow in drones

- Advisor for start-up company on developing new uses for tactile displays

- Team member in design of emotion recognition and training for autistic children

Internship

Half of your second year consists of an internship, giving you plenty of hands-on experience. We encourage students to do this internship abroad, although this is not mandatory. We do have connections with companies abroad, for example in China, Finland and the United States.

See the website http://www.ru.nl/masters/ai/robot



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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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Data science brings together computational and statistical skills for data-driven problem solving, which is in increasing demand in fields such as marketing, pharmaceutics, finance and management. Read more

Data science brings together computational and statistical skills for data-driven problem solving, which is in increasing demand in fields such as marketing, pharmaceutics, finance and management. This MSc will equip students with the analytical tools to design sophisticated technical solutions using modern computational methods and with an emphasis on rigorous statistical thinking.

About this degree

The programme combines training in core statistical and machine learning methodology, beginning at an introductory level, with a range of optional modules covering more specialised knowledge in statistical computing and modelling. Students choosing the statistics specialisation will take one compulsory module and up to two additional modules in computer science, with the remaining modules (including the research project) taken mainly from within UCL Statistical Science.

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 dissertation/report (60 credits).

Core modules

  • Introduction to Statistical Data Science
  • Introduction to Machine Learning
  • Statistical Design of Investigations
  • Statistical Computing

Optional modules

At least two from a choice of Statistical Science modules including:

  • Applied Bayesian Methods
  • Decision & Risk
  • Factorial Experimentation
  • Forecasting
  • Quantitative Modelling of Operational Risk and Insurance Analytics
  • Selected Topics in Statistics
  • Stochastic Methods in Finance I
  • Stochastic Methods in Finance II
  • Stochastic Systems

Up to two from a choice of Computer Science modules including:

  • Affective Computing and Human-Robot Interaction
  • Graphical Models
  • Statistical Natural Language Processing
  • Information Retrieval & Data Mining

Dissertation/report

All students undertake an independent research project, culminating in a dissertation usually of 10,000-12,000 words. Workshops running during the teaching terms provide preparation for this project and cover the communication of statistics.

Teaching and learning

The programme is delivered through a combination of lectures, tutorials and classes, some of which are dedicated to practical work. Assessment is through written examination and coursework. The research project is assessed through the dissertation and a 15-minute presentation.

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

Careers

Graduates from UCL Statistical Science typically enter professional employment across a broad range of industry sectors or pursue further academic study. 

The Data Science MSc is a new programme with the first cohort of students due to graduate in 2017. Recent career destinations for graduates of the related Statistics MSc include:

  • Management Associate, HSBC
  • Statistical Analyst, Nielsen
  • PhD in Statistics, UCL
  • Mortgage Specialist, Citibank
  • Research Assistant Statistician, Cambridge Institute of Public Health

Employability

Data science professionals are likely to be increasingly sought after as the integration of statistical and computational analytical tools becomes essential in all kinds of organisations and enterprises. A thorough understanding of the fundamentals is to be expected from the best practitioners. For instance, in applications in marketing, the healthcare industry and banking, computational skills should should be accompanied by statistical expertise at graduate level. Data scientists need a broad background knowledge so that they will be able to adapt to rapidly evolving challenges. Recent graduates from the related Statistics MSc have been offered positions as research analysts or consultants, and job opportunities in these areas are increasing.

Why study this degree at UCL?

UCL Statistical Science has a broad range of research interests, but has particular strengths in the area of computational statistics and in the interface between statistics and computer science.

UCL's Centre for Computational Statistics and Machine Learning, in which many members of the department are active, has a programme of seminars, masterclasses and other events. UCL's Centre for Data Science and Big Data Institute are newer developments, again with strong involvement of the department, where emphasis is on research into big data problems.

UCL is one of the founding members of the Alan Turing Institute, and both UCL Statistical Science and UCL Computer Science will be playing major roles in this exciting new development which will make London a major focus for big data 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: Statistical Science

82% 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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The Human-Computer Interaction (HCI) programme at Tallinn University is a multidisciplinary curriculum that emphasises technology for the benefit of people.This curriculum brings together computing, design and cognitive psychology. Read more

The Human-Computer Interaction (HCI) programme at Tallinn University is a multidisciplinary curriculum that emphasises technology for the benefit of people.This curriculum brings together computing, design and cognitive psychology. It offers a research-based approach to designing interactive, software and technical systems.It enables you to shape the world through what you design.

Who are we looking for?

We welcome students with a wide variety of backgrounds. We favour everyone who is interested in improving the way technology is made available to people and intertwined with their lives. We favour:

  • Developers
  • Designers
  • Anthropologists
  • Psychologists

Why study with us?

This is your chance to become a well grounded Human-Computer Interaction specialist, able to act as a scholarly design researcher, a knowledgeable interaction designer, or a discerning user experience professional. It’s an opportunity to mould your future, our future, and study in the most E of all countries, Estonia.

Not only will you be able to systematically go from an idea, opportunity or challenge, to a technology-based solution, you will also be able to do it based on sound theoretical grounds. You will:

  • Combine computational thinking with design thinking
  • Integrate academic and practitioner perspectives

Our programme starts with a sound and thorough introduction to the field of Human-Computer Interaction, moves on to a semester long integrated interaction design project and rounds up with topics such as:

  • Ambient and ubiquitous computing
  • Physiological and affective computing
  • Perception and attention
  • Cognition and emotion

The capstone is your master thesis. Research-based, practice-base, many configurations are possible but surely it will be a in-depth experience.



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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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The MRes covers web-related technologies and big data analytics. It is intended for students with a general science and engineering background and is an ideal preparation for roles with some of the best employers in internet-related industries and areas requiring big data analytical skills. Read more

The MRes covers web-related technologies and big data analytics. It is intended for students with a general science and engineering background and is an ideal preparation for roles with some of the best employers in internet-related industries and areas requiring big data analytical skills.

About this degree

Students will gain a detailed knowledge and understanding of the fundamental principles and technological components of the World Wide Web, learning not only the latest web search and information retrieval technologies and their underlying computational and statistical methods, but also studying essential large-scale data analytics to extract insights and patterns from vast amounts of unstructured data.

Students undertake modules to the value of 180 credits.

The programme consists of two core modules (30 credits), either four optional modules (60 credits) or three optional and one elective module, and the research dissertation (90 credits).

Core modules

  • Investigating Research (15 credits)
  • Researcher Professional Development (15 credits)

Optional modules

Students must choose a minimum of 45 and a maximum of 60 credits of optional modules. Students may also choose up to 15 credits from electives.

  • Affective Computing and Human-Robot Interaction (15 credits)
  • Complex Networks and Web (15 credits)
  • Computer Graphics (15 credits)
  • Graphical Models (15 credits)
  • Information Retrieval and Data Mining (15 credits)
  • Machine Vision (15 credits)
  • Probabilistic and Unsupervised Learning (15 credits)
  • Statistical Natural Language Processing (15 credits)
  • Web Economics (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 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 help from demonstrators. For the research project, each student liaises with their academic or industrial supervisor to choose a study area of mutual interest. Student performance is assessed by unseen written examinations, coursework and the research dissertation.

Careers

Graduates from UCL are keenly sought after by the world's leading organisations, and many progress in their careers to secure senior and influential positions. Graduates of our Web Science and Big Data Analytics programmes are expected to develop careers in scientific research, the internet-based industry and other professional areas that require big data analytics skills.

Recent career destinations for this degree

  • Software Developer, British Film Institute (BFI)
  • Software Developer, Geneity

Employability

The MRes has a unique industry connection as almost all our lecturers have industry experience. Through long-term collaborations with big players in the field such as Google, Microsoft and BT, their research is driven by the fundamental technical challenges faced by the industry. Throughout the degree, our students have the chance to interact with our industry collaborators and previous students, and have placement opportunities to address specific technical problems faced by the industry.

Why study this degree at UCL?

UCL Computer Science is recognised as a world leader in teaching and research. 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 Master's programmes have some of the highest employment rates and starting salaries, with graduates entering a wide variety of industries, from entertainment to finance.



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Technology Studies Education provides a forum for exploring and studying information and communication technologies (ICT), new media, and the philosophy of technology. Read more

Program Overview

Technology Studies Education provides a forum for exploring and studying information and communication technologies (ICT), new media, and the philosophy of technology. Curriculum, pedagogy, research, and development interests of faculty and students include affective computing, cyberculture and cyborg relations, digital ecology and diversity, distributed cognition, gaming, ICT integration in K–16 formal and informal learning environments (face-to-face, hybrid, and online distance education), intellectual property, open source, and cultural studies. The program offers a common core of courses, a range of electives, and a variety of professional education opportunities.

Quick Facts

- Degree: Master of Arts (research-based), Master of Education (course-based)
- Specialization: Media and Technology Studies Education
- Subject: Education
- Mode of delivery: On campus
- Faculty: Faculty of Education

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The graduate programs (M.Ed. and M.A) in Media and Technology Studies Education are part of the graduate offerings in the Department or Curriculum and Pedagogy. Read more

Program Overview

The graduate programs (M.Ed. and M.A) in Media and Technology Studies Education are part of the graduate offerings in the Department or Curriculum and Pedagogy. Media and Technology Studies provides a forum for exploring and studying information and communication technologies (ICT), new media, and the philosophy of technology. Curriculum, pedagogy, research, and development interests of faculty and students include affective computing, cyberculture and cyborg relations, digital ecology and diversity, distributed cognition, gaming, ICT integration in K-16 formal and informal learning environments (face-to-face, hybrid, and online distance education), intellectual property, open source, and cultural studies. The program offers a common core of courses, a range of electives, and a variety of professional education opportunities.

The Department offers a Sub-specialization in Human-Computer Interaction (HCI) in conjunction with the Media and Graphics Interdisciplinary Centre (MAGIC), which is available to students in the Media and Technology Studies Education program.

Quick Facts

- Degree: Master of Education
- Specialization: Technology Studies Education
- Subject: Education
- Mode of delivery: On campus
- Program components: Coursework + Major Project/Essay required
- Faculty: Faculty of Education

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Who is it for?. This Masters is ideal for those who have an undergraduate degree in Psychology or a related discipline and would like to build more knowledge and skills highly valued both in academic research and the clinical professions. Read more

Who is it for?

This Masters is ideal for those who have an undergraduate degree in Psychology or a related discipline and would like to build more knowledge and skills highly valued both in academic research and the clinical professions. The MSc is an ideal platform from which to progress to PhD studies, particularly in Cognitive or Social Neuroscience. Students will also be well-equipped should they wish to undertake further professional training in Clinical Psychology, or a related discipline.

Objectives

This Masters degree bridges three research and clinical disciplines:

  • Cognitive Neuroscience (the study of human brain functions such as memory, perception and language)
  • Clinical Neuroscience (the understanding of neurological, psychological or psychiatric illness via their neural and cognitive antecedents)
  • and Social Neuroscience (the investigation of brain processes that help us communicate, feel, learn and interact with others).

The major aim of this programme is to provide you with a thorough grounding in the neuroscience that underpins human cognitive brain function, clinical, social and affective interaction, and neuropathology.

Teaching will comprise of seminars, lectures, computing and statistics classes, and supervision of an individual research project. Your learning experience during the programme will be enhanced by an invited speaker’s programme of external experts who work in Clinical, Social or Cognitive neuroscience.

Academic facilities

You will have access to all the facilities and laboratories in the Psychology Department. Check our labs facilities in the Cognitive Neuroscience Research Unit (CRNU)the Baby lab, the Autism Research Group (ARG), the Human Memory Research Group, etc. For a full list of facilities visit the Psychology Department.

Our members have experience with a wide range of neuroscientific techniques, including neuropsychological testing, psychophysics, electrophysiology, and neuroimaging methods.  We have particular strengths in the use of Electroencephalography (EEG)Transcranial magnetic stimulation (TMS) and Transcranial Electric Stimulation (a weak current applied to the scalp), in addition to measures of human behaviour (e.g. response times, response errors, and eye movements) and physiological measures (e.g. galvanic skin response and heart rate).

We test neurologically normal individuals, special populations (e.g. people with synesthesia) and people with expertise or acquired skills (e.g. dancers, musicians, athletes), as well as people with brain damage (e.g. neglect or split-brain patients), psychiatric diagnoses (e.g. schizophrenia), sensory deficits (e.g. visual and hearing impairments) and developmental disorders (e.g. dyslexia or autism).

Placements

We facilitate clinical internships through our specialist research Centre for Psychological Wellbeing and Neuroscience (CPWN) and with the local Mind centre.

Teaching and learning

Teaching will be comprised of lectures, seminars, group work and discussions, workshops and tutorials, reports, computing and statistics classes and the individual research dissertation.

You will undertake independent study, supported by the teaching and learning team, and will receive detailed feedback on your coursework. You will be provided with assessment and grade-related criteria which will outline your intended learning outcomes, along with the skills, knowledge and attitudes you are expected to demonstrate in order for you to complete an assessment successfully. You will also be assigned a personal tutor as your primary contact, who will advise you on academic matters and monitor your progress through the programme.

You will find a supportive vibrant research environment in the Department. The course is taught by academics, who are internationally recognised experts in their field with different backgrounds in clinical, social and cognitive neuroscience.

Check out what is going on in our laboratories and at the Center for Psychological Wellbeing and Neuroscience (CPWN).

Find our more about our work on our Facebook group.

Assessment

Your learning will be assessed through essays, examinations, oral presentations, research methods projects and interpretation of statistical analyses, formal research proposals and a dissertation.

Modules

The programme consists of eight taught modules worth 15 credits each with around 30-34 hours of face-to-face contact, supported by online resources and an empirical research project (worth 60 credits).

You will learn about the latest advances in clinical, social and cognitive neuroscience and develop an appreciation of the reciprocal nature of research and practice in these domains. For example how insights from functional neuroimaging inform our understanding of neurological disorders and how clinical observations inform neurocognitive modelling.

Career prospects

This course will provide you with knowledge and skills highly valued both in academic research and the clinical professions. The MSc is an ideal platform from which to progress to PhD studies, particularly in Cognitive or Social Neuroscience. You will also be well-equipped should you wish to undertake further professional training in Clinical Psychology, or a related discipline.

The knowledge and skills you will acquire in this programme are highly valuable, whether you choose to pursue further research or an applied occupation. They will enhance your employability prospects in a wide range of sectors including the pharmaceutical industry, neuromarketing, the computing industry, science and the media, science and the arts, business or education.



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