Oswal Group
Our aim is to provide a detailed understanding of how brain network activity at various timescales contributes to movement and cognitive function, both in health and in neurological diseases. Neurological symptoms are not static, but change over minutes, hours, or days, meaning that an understanding of the origins of their dynamics is necessary for the development of improved therapies. We aim to: (1) relate nerve cell activity within particular brain networks to specific symptoms and (2) modulate this activity in order to improve clinical outcomes.
We adopt a multidisciplinary approach to understand how communication within brain networks contributes to both normal and pathological brain health. To this end, we study healthy individuals and patients with neurological conditions that affect movement and cognition e.g., Parkinson’s disease, and other neurological conditions such as dementia.
Some of the patients we study have undergone a surgical procedure known as Deep Brain Simulation (DBS), during which electrodes are inserted deep into the brain to allow for therapeutic electrical stimulation. By recording from DBS electrodes and from cortical brain areas non-invasively (using EEG or MEG) it is possible to study cortico-basal ganglia interactions and reveal their modulation by DBS. A key goal of our research is to carefully characterise the network modulatory effects of DBS, in the hope that these can be reproduced using novel minimally-invasive techniques.
A parallel strand of our research leverages imaging modalities with high spatial and temporal resolution to reveal network disturbances that underly impairments of memory and motivation in patients. The neuronal mechanisms underlying these symptoms are presently poorly understood, and there are significant opportunities for improving treatments.
- Oscillatory dynamics underlying motor, memory, and motivational impairments in neurological disease
- Predicting pathological signals for Deep Brain Stimulation
- Refining Deep Brain Stimulation for symptom control
- Neuroimaging & neurophysiological techniques (MEG, EEG, MRI)
- Invasive recordings (local field potentials & ECOG)
- Deep Brain Stimulation
- Computational modelling, Machine Learning & signal processing
Mary Muers (far right) moderates an interactive session about training and career development opportunities.
Studentships
Project
Discovering and translating physiological biomarkers for personalised adaptive Deep Brain Stimulation in Parkinson’s disease
Deep Brain Stimulation (DBS) is an established therapy for Parkinson’s disease (PD), but its benefits vary across symptoms and between individuals. New generations of DBS devices can record brain activity as well as deliver stimulation, providing an opportunity to understand how neural dynamics relate to symptoms and treatment response. Identifying robust neural and behavioural biomarkers could enable stimulation to be adapted to an individual’s changing clinical state and improve treatment of both motor and non-motor symptoms.
The overall goal of this PhD studentship is to determine how neural and behavioural signals can be used to guide more personalised adaptive Deep Brain Stimulation for Parkinson’s disease. The project will combine brain recordings with measures of movement and behaviour across rest, movement, and sleep in people implanted with sensing-enabled DBS devices. Advanced analytical approaches will be used to identify biomarkers of clinically relevant states and to investigate how these biomarkers can inform new stimulation strategies. In doing so, the project aims to generate new mechanistic understanding of Parkinson’s disease while helping develop more responsive and personalised neurostimulation therapies.
The project will take place in the Brain Network Dynamics Unit of the Nuffield Department of Clinical Neurosciences and in the Medical Research Council Centre of Research Excellence in Restorative Neural Dynamics (MRC CoRE RND). Students will benefit from the extensive interdisciplinary skills training and personalised career development opportunities available within the Unit and the MRC CoRE RND. Students will receive specialised training in their areas of project research (see below) as well as, for example, in the translation and commercialisation of research, best practice in Open Science, and how to effectively involve and engage patients and the public with research.
Focusing on translational human neurotechnology, this studentship offers the opportunity to work with cutting-edge techniques including wireless neural activity streaming from implanted DBS devices, high density EEG, EMG, and wearable sensor recordings. You will develop advanced skills in signal processing, multimodal data integration, machine learning, patient-facing research, and the design and testing of adaptive stimulation paradigms. The project offers close collaboration with the functional neurosurgery service at Oxford University Hospitals, North Bristol NHS Trust, industrial partners Amber Therapeutics, and people affected by Parkinson’s. There will also be opportunities to collaborate through the Parkinson’s UK Deep Brain Stimulation Network and contribute to shared translational datasets.
This four-year Ph.D. (D.Phil.) studentship offers three years of full-time tuition fees at the Home rate, and four years of non-taxable stipend at the full-time UKRI rate (including any uplifts announced). Both Home students and International students are eligible to receive this funding package. Please see further details about MRC/UKRI studentships and UKRI guidance regarding Home and International eligibility. Successful offer-holders who have applied by the December deadline may also be considered for other University of Oxford scholarships.
Interested candidates should possess, or expect to receive, a 1st class or upper 2nd class degree (or equivalent) in a related scientific discipline, e.g. biological or physical sciences, medicine, computer science, engineering, mathematics. Applicants from experimental, clinical, engineering and computational backgrounds are encouraged.
Relevant experience in one or more of the following areas is desirable:
- Research experience in neuroscience, neurophysiology or a related field
- Signal processing or analysis of neural, physiological, movement or other biological data
- Programming for data analysis, for example in Python, MATLAB or R
Experience of human or patient-facing data collection, wearable or movement-sensor analysis, machine learning, or brain stimulation would be advantageous but is not essential.
Candidates must contact the lead project supervisor before submitting an application. To find out more about this studentship, the research project, and the application process, please contact Dr Ashwini Oswal by email on ashwini.oswal@ndcn.ox.ac.uk.
To be considered for this studentship, please submit an application for admission to the D.Phil. in Clinical Neurosciences at the Nuffield Department of Clinical Neurosciences (course code RD_CU1), following the guidance for applications to this course. On the application form, in the section headed ‘Departmental Studentship Applications’, please indicate that you are applying for a studentship and enter the reference code “27NDCN01MRC” into the funding tab.
The closing date for applications is 12.00 midday UK time on Tuesday 1st December 2026.
Supervisors
Applications are invited from both Home students and International students to join a multidisciplinary team of researchers studying how neural, movement and behavioural signals can be used to personalise adaptive deep brain stimulation (DBS) for Parkinson’s disease. This studentship is available from the start of academic year 2027/28, is for 4 years, and will be co-supervised by Dr. Ashwini Oswal, Professor Tim Denison, Dr. Hayriye Cagnan, and Dr. Bahman Abdi-Sargezeh at the MRC Centre of Research Excellence in Restorative Neural Dynamics.
Recent Publications
Like other Groups at the BNDU, we are committed to best practice in open research. We have created and curated a range of primary data, metadata and related resources that can be readily downloaded by external users from the BNDU's data sharing platform, Cambium.