Research Associate, Department of Engineering Mathematics
Division/School School of Computer Science, Electrical and Electronic Engineering and Engineering Maths
Contract type Fixed Term Contract
Working pattern Full time
Salary £33,199 - £37,345
Closing date for applications 19-Mar-2019
Based within the Department of Engineering Mathematics at the University of Bristol, with Dr Naoki Masuda you will analyse the preprocessed MEG and sEEG data using recurrence analysis.
We are looking for a highly-motivated, self-driven PhD in computer science, physics, applied mathematics, biomedical engineering or a closely related field. You should be interested in developing and applying computational approaches for processing and analyzing MEG and EEG data within the scope of the project described above. Some background in either computational neuroscience, machine learning, statistical modelling, network science or nonlinear dynamics is required.
General knowledge on MEG, EEG or epilepsy will be considered as a plus. Skills in Python or MATLAB will also be considered as a plus.
The aim of the project is to consolidate the methodology based on “recurrence analysis” developed in a recently funded pilot project and apply it to functional network data recorded from human individuals affected by epilepsy. Recurrence analysis is a well-established framework for nonlinear numerical time series (Marwan et al., Physics Reports, 438, 237, 2007), which allows us to (i) obtain visual information by means of two-dimensional “recurrence plots” and (ii) quantify different aspects of the dynamics by means of the so-called “recurrence quantification analysis”.
We see strong potentials of our recurrence analysis methods in epilepsy for a number of reasons. Therefore, we will apply the methods to temporal, functional connectivity network data obtained with MEG and also a type of EEG, called stereo EEG. Both data sets have been obtained from epileptic participants and healthy controls in Cardiff. The project aims to (i) characterise patients’ resting-state MEG signals and classify them from controls, and (ii) localise stereo EEG signatures in the form of temporal network changes during an inter-ictal to pre-ictal transition.
For informal enquires please contact Dr Naoki Masuda firstname.lastname@example.org
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