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Dynamics on/of networks and oscillopathies


School of Engineering and Informatics

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Prof L Berthouze , Dr G Parisis No more applications being accepted Funded PhD Project (Students Worldwide)

About the Project

Profile Description

A number of conditions, such as Parkinson’s Disease, are referred to as ‘oscillopathies’ because there is evidence that some of their major symptoms are underpinned by pathological synchronisation of neurons in key networks. A promising form of treatment is that of online control of deep brain stimulation based on computational processing of brain signals. Therefore, new mathematical insights about how to detect and efficiently disrupt pathological processes can be rapidly translated to patient benefit.Using models of oscillations on networks, this project will seek to investigate the conditions (either on the oscillators themselves, or the connectivity, or factors that may frustrate the interplay between dynamics on and dynamics of the network) under which pathological oscillations in one small area of the brain can intermittently or otherwise ‘infect’ the wider network of the brain causing the pathological symptoms. This project will involve an active collaboration with Dr Simon Farmer at UCL.

The research will take place under the umbrella of an active and well-funded collaboration between Prof Luc Berthouze (Complex Systems; AI research group) and Dr George Parisis (Computer Networks; FOSS group) within the Department of Informatics, at the University of Sussex, UK. Interested candidates will also have the opportunity to collaborate with academic and industry partners of the supervisors as well as others. Starting date is flexible.

Eligibility
You will have a first-class honour degree or equivalent, or an MSc degree (preferably), in Computer Science, Mathematics or Physics (or relevant disciplines). You will have (and enjoy using) strong analytical skills as well as excellent programming skills in at least one of C++, Python, Matlab.

You will have a good knowledge of English and be able to express yourself clearly in both written and spoken form. You will have had a long-standing interest in pursuing doctoral studies, and be enthusiastic about delving into and contributing to the exciting and fast-moving research area that network science is. Ideally, you will have had some experience working across disciplines, perhaps, during a research internship, a final year project or an MSc dissertation.

You will have shown the ability to work both as part of a team and independently. Depending on the nature of your topic, you will have knowledge and experience in at least one of the following fields: graph theory, machine learning, probability theory / stochastic processes, big data analysis. Domains in which you will have applied this knowledge could include (but not be limited to): computer networks, neuroscience, epidemiology, analysis of social media.

Entry requirements: https://www.sussex.ac.uk/study/phd/apply

How to apply
Apply online for a full time PhD in Informatics using our step by step guide (http://www.sussex.ac.uk/study/phd/apply). Here you will also find details of our entry requirements.

Please clearly state on your application form that you are applying for a ’Dynamics on/of networks and oscillopathies’.

Please note that we request a ‘Statement of Research Interests’. Your statement (no more than 500 words) should answer two questions:(i) Why are you interested in the topic described above?(ii) What relevant experience do you have? In addition to this, we would also like you to submit a sample of your written work. This might be a chapter of your final year or masters dissertation or a published paper.

Contact us
Applicants interested in the post, seeking further information or feedback on their suitability are encouraged to contact Prof Luc Berthouze and Dr George Parisis. All applications must be made via the website mentioned above.

Funding Notes

All nationalities are eligible to apply for this studentship. This 3-year studentship includes student fees and a tax-free stipend starting at £15,285 per annum. It also comes with a generous Research Training Support Grant.


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