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Masters Degrees (Neurotechnology)

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This course provides a grounding in neurotechnology research, and enables you to develop the technical skills required to carry out successful PhD work. Read more

This course provides a grounding in neurotechnology research, and enables you to develop the technical skills required to carry out successful PhD work.

Neurotechnology is the use of insights and tools from mathematics, physics, chemistry, biology and engineering to investigate neural function and treat dysfunction. Brain-related illnesses affect more than two billion people worldwide, and the numbers are growing. Reducing this burden is a major challenge for society.

The MRes Neurotechnology is a one year full-time programme which will provide you with technical knowledge, expertise and transferable skills in this exciting area. Our MRes will prepare you for an innovative research career at the interface between neuroscience and engineering, in academia, industry, or elsewhere.

The programme comprises lectures, practical work and workshops in the first term, followed by full-time work on a research project. In addition, you will attend regular symposia and a neurotechnology journal club throughout the year. Your research project will be supervised by a multidisciplinary team of at least two Imperial College London supervisors who will bring technical and neuroscience expertise to the project. 

The MRes Neurotechnology also forms the first year of the four-year CDT Neurotechnology programme. Students on the one-year course benefit from interaction with CDT cohorts throughout the programme.

Careers

The MRes Neurotechnology provides unique training at the interface between neuroscience and engineering, enhancing your skills in research, analysis and scientific presentation and developing your ability to work in a multidisciplinary team.

Our programme provides a solid foundation in research for those continuing with a neurotechnology career, whether within the CDT Neurotechnology programme or a separate PhD, or working in industry.

Contacting a supervisor

Please note that you must contact a potential academic supervisor before applying. For more information on what you need do to before you apply and anything else about the course, see: http://www.imperial.ac.uk/study/pg/bioengineering/neurotechnology-mres/

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



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Our Master's degree in Biomedical Engineering first began in 1991 and provides all of the necessary technical knowledge, expertise and transferable skills to succeed in one of the fastest growing engineering disciplines. Read more

Our Master's degree in Biomedical Engineering first began in 1991 and provides all of the necessary technical knowledge, expertise and transferable skills to succeed in one of the fastest growing engineering disciplines.

This degree offers four distinct steams, each of which accredited and employment-focused:

  • Biomechanics pathway – this stream is focused on the bioengineering problems related to major diseases associated with an ageing population
  • Biomaterials pathway – this stream is jointly offered with the Department of Materials and covers the use of biomaterials in medical and surgical environments
  • Medical Physics pathway – this stream trains graduates in the physical understanding required for healthcare and medical research, with focus on human physiology, radiotherapy and clinical imaging
  • Neurotechnology pathway – this stream focuses on new technology for investigating brain function, such as developing neuroprosthetic devices and new neuroimaging techniques

You choose your stream during the application process and all four streams lead to the award of MSc Biomedical Engineering.

Careers

Our career-focused degrees ensure graduates are well-placed to gain employment in a growing industry. The global population is ageing which increases demand for biomedical engineers to create new medical devices.

There are many areas of employment open to you as a graduate of this course, and previous graduates have gone on to pursue careers in healthcare, the medical device industry, research, medicine, start-ups, teaching, consultancy and finance.

More information

For full information on this course, including how to apply, see: http://www.imperial.ac.uk/study/pg/bioengineering/biomedical-engineering-msc/

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



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Learn how to create artificial information systems that mimic biological systems as well as how to use theoretical insights from AI to better understand cognitive processing in humans. Read more

Learn how to create artificial information systems that mimic biological systems as well as how to use theoretical insights from AI to better understand cognitive processing in humans.

The human brain is a hugely complex machine that is able to perform tasks that are vastly beyond current capabilities of artificial systems. Understanding the brain has always been a source of inspiration for developing artificially intelligent agents and has led to some of the defining moments in the history of AI. At the same time, theoretical insights from artificial intelligence provide new ways to understand and probe neural information processing in biological systems.

On the one hand, the Master’s in Neural Computing addresses how models based on neural information processing can be used to develop artificial systems, such as neuromorphic hardware and deep neural networks, as well as the development of new machine learning and classification techniques to better understand human brain function and to interface brain and computer.

On the other hand it addresses various ways of modelling and understanding (the limitations of) cognitive processing in humans. These range from abstract mathematical models of learning that are derived from Bayesian statistics to resource-bounded computations in the brain, explainable AI, and neural information processing systems such as neural networks that simulate particular cognitive functions in a biologically inspired manner.

See the website http://www.ru.nl/english/education/masters/neural-computing/

Why study Neural Computing at Radboud University?

- 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.

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

- Master’s students are free to use the state-of-the-art facilities available on campus, like equipment for brain imaging as EEG, fMRI and MEG.

- Exceptional students who choose this specialisation have the opportunity to study for a double degree in Artificial Intelligence together with the specialisation in Brain Network and Neuronal Communication. This will take three instead of two years.

- 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 will be able to use these facilities for developing new experimental research techniques, as well as for developing new machine learning algorithms to analyse the brain data and integrate them with brain-computer interfacing systems.

- Deep learning

Recent breakthroughs in AI have led to the development of artificial neural networks that achieve human level performance in object recognition. This has led companies like Google and Facebook to invest a lot of research in this technology. Within the AI department you can do research on this topic. This can range from developing deep neural networks to map and decode thoughts from human brain activity to the development of speech recognition systems or neural networks that can play arcade games.

-Computational framework for counterfactual predictive processing

In a recent paper we introduced a computational framework, based on causal Bayesian networks, to computationally flesh out the predictive processing processing framework in neuroscience. In this project we want to extend this to so-called counterfactually rich generative models in predictive processing. Such models encode sensorimotor contingencies, that is, they represent 'what-if' relations between actions and sensory inputs. We aim to further operationalize this account using Pearl's intervention and counterfactual semantics. In this project you will combine formal computational modelling with conceptual analysis. 

- Brain Computer Interfacing

Brain computer interfaces are systems which decode a users mental state online in real-time for the purpose of communication or control. An effective BCI requires both neuro-scientific insight and technical expertise . A project could be to develop new mental tasks that induce stronger/easier to decode signals, such as using broadband stimuli. Another project could be to develop new decoding methods better able to tease a weak signal from the background noise, such as adaptive-beam forming. Results for both would assessed by performing empirical studies with target users in one of the EEG/MEG/fMRI labs available in the institute.

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: Google, Facebook, IBM, Philips and the Brain Foundation. Some students have even gone on to start their own companies or joined recent startups.

Job positions

Examples of jobs that a graduate of the specialisation in Computation in Neural and Artificial Systems could get:

- PhD researcher on bio-inspired computing

- PhD researcher on neural decoding

- PhD researcher on neural information processing

- Machine learning expert in a software company

- Company founder for brain-based computer games

- Hospital-based designer of assistive technology for patients

- Policy advisor on new developments in neurotechnology

- Software developer for analysis and online visual displays of brain activity

Internship

Instead of an extended research project (45 ec) you can also choose to do a smaller (30 ec) research project plus a 15 ec internship, giving you plenty of hands-on experience with AI. We encourage students to do this internship abroad.



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This course specialises in sophisticated data mining and machine learning techniques, exploiting scalable data management and processing infrastructures, including neurotechnology, bioinformatics, security and human-centered computing. Read more

This course specialises in sophisticated data mining and machine learning techniques, exploiting scalable data management and processing infrastructures, including neurotechnology, bioinformatics, security and human-centered computing.

This taught postgraduate course is aimed at students who may not have studied computing exclusively, but who have studied a considerable amount of computing already.

If you want to become a specialist in a particular area of computing, this course will provide a first crucial step towards that goal.

Further information

For full information on this course, including how to apply, see: http://www.imperial.ac.uk/study/pg/computing/machine-learning/

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



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