DPhil Studentship: How Brain Network Dynamics Shape Human Cognition

How Brain Network Dynamics Shape Human Cognition

DPhil in Cognitive Neuroscience, Department of Psychiatry, University of Oxford | October 2027

This fully funded Department of Psychiatry project investigates how the timing and coordination of brain network activity shape human cognition. Combining magnetoencephalography (MEG), computational modelling and cognitive experiments, it aims to uncover the neural dynamics underlying flexible behaviour.

The Group

You will join Dr Mats van Es' research group at the Oxford Centre for Human Brain Activity (OHBA), part of the Oxford Centre for Integrative Neuroimaging (OxCIN). Building on our recent discovery of recurring cycles of brain network activity, we develop computational approaches to understand their underlying mechanisms, functional significance and potential for intervention.

The DPhil

The project, refined with the successful candidate, will investigate how brain network dynamics vary across cognitive contexts and whether their temporal organisation constrains behavioural performance.

You will analyse large MEG datasets to characterise how networks interact and transition over time, and test whether these dynamics predict reaction times and accuracy during memory, perception and decision-making tasks. You will also help design and conduct new MEG experiments to test hypotheses about the functional role of network timing.

Depending on your interests, the project may extend to computational modelling or closed-loop experiments, in which ongoing brain activity determines the timing of task events. These approaches offer opportunities to test whether manipulating network timing can influence cognition and behaviour.

Training/supervision

You will receive training in advanced MEG analysis, computational modelling of brain dynamics (including machine learning methods), experimental design and statistical inference. You will develop skills in formulating research questions, open-science and reproducible research, and communicating scientific findings.

You will benefit from close supervision, an interdisciplinary research environment and access to state-of-the-art MEG, MRI and neuromodulation facilities and high-performance computing. Opportunities for international collaboration and conference presentations will support your scientific independence and career development.

Candidate profile

We welcome applicants with at least an upper second-class honours degree (or equivalent) and/or a relevant Master's degree in cognitive neuroscience, psychology, neuroscience, physics, engineering, computer science or related fields.

We seek candidates with a strong interest in human cognition, excellent quantitative and computational skills (ideally Python), and experience with electrophysiology (MEG or EEG). Experience in experimental design, cognitive modelling or machine learning is advantageous. Independent research experience, scientific curiosity, and strong communication and collaboration skills are important. We are committed to equality, diversity, and inclusion and encourage applications from all backgrounds.

Funding/application

The scholarship covers course fees up to the value of home fees*, a tax-free stipend at the UKRI standard rate (no less than £22,460 per annum), plus additional support for research expenses and conference attendance.

*Applicants with overseas fee status would need to fund the fee difference from alternative sources.

Applications must be submitted via the University’s online graduate application form. To access the application form and guidance please visit our website at www.graduate.ox.ac.uk/apply. The application form, all supporting materials (including references), and £20 application fee must be received by the submission deadline.

For informal enquiries please contact: mats.vanes@psych.ox.ac.uk

Submission deadline: 12:00 noon UK time on Friday 27^th November 2026 (NOTE: this is earlier than the University’s December deadline)

Interview date w/c 14^th December 2026

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