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PhD Studentship: Unifying Multiscale Brain Network Interactions (SAMI_U19FMHMTH)

University of East Anglia - Norwich Medical School

Qualification Type: PhD
Location: Norwich
Funding for: UK Students, EU Students
Funding amount: £15,009 per annum
Hours: Full Time
Placed On: 21st August 2019
Closes: 31st October 2019

Location: Norwich
Start Date: January 2020
Supervisor: Dr Saber Sami (Primary supervisor), Dr Davide Proment (Secondary supervisor)

Project description:

IMPORTANT: Applications for this project will open on the UEA system w/c 2 September 2019.

We offer a fully funded inter-disciplinary PhD project that focuses on the development of new mathematical models and their application in cognitive and clinical neuroscience. The project brings together several strands of statistical mechanics and non-linear dynamics with computational neuroscience to improve current mathematical models of healthy ageing and neurodegeneration.

This scheme attempts to bridge the gap between molecular biology and the complex disease syndromes by addressing an important missing link through unifying multi-scale brain network interaction. The project will allow the prospective PhD candidate to combine experimental work from functional MRI & EEG with computational models of healthy ageing and disease.

The work will require the prospective PhD candidate to actively collaborate with clinical and mathematical research groups at the University of East Anglia, and several research institutions.

Our Offer:

Engagement with international multidisciplinary teams in highly ambitious projects in an inspiring and collaborative environment.

Various opportunities for further education, training and professional growth.

 Essential Criteria

Excellent programming skills, experience in at least one of the following: MATLAB, Python, Java/C++, and C/Fortran.

A solid background in statistical mechanics &/or non-linear physics/mathematics.

Excellent command of written and spoken English


Previous experience in areas of statistical mechanics, non-linear physics/mathematics, graph theory or signal processing (e.g., time series analysis) would be beneficial.

Knowledge of machine learning techniques

Person specification:

Acceptable first degree: B/MSc in Physics, Mathematics, Engineering, Computer Science.

The standard minimum entry requirement is 2:1. An MSc qualification will be advantageous.

Funding notes:

This PhD studentship is jointly-funded by Norwich Medical School and the School of Mathematics. Funding comprises Home/EU fees and a stipend of £15,009 and £1000 per annum to support research training.

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