UON Fully Funded PhD Opportunity within mental health & neuroscience research
Application Deadline: Tuesday 22nd July 2025
Interviews to take place Week Commencing 11th August 2025
We are seeking an enthusiastic and highly motivated healthcare professional to undertake a fully funded 36-month project within mental health or translational neuroscience research at the University of Nottingham. The successful candidate will join a vibrant community of postgraduate researchers and will benefit from attending structured academic and professional development training courses provided through the School of Medicine and/or the University of Nottingham Researcher Academy network; as well as access to Patient and Public Involvement and Engagement (PPIe) and other research support linked with the Midlands Mental Health DTP.
We have five unique projects available to choose from, as detailed below. Please review the key information as listed prior to contacting a project supervisor to discuss your suitability for the position.
Studentship Details
- One studentship available
- 36-month PhD
- 1st October 2025 start date.
- Tuition fees funded for 36 months.
- Salary (NHS equivalent, up to Band 5 entry level – £31,049)
- A research training support grant.
- Professional development training opportunities.
Entry Requirements
- Applicants must be registered healthcare professionals.
- Applicants must be a Home student for tuition fee purposes.
- You must meet the entry requirements as outlined on the PhD Medicine webpage – https://www.nottingham.ac.uk/pgstudy/course/research/medicine-phd
- If English is not the candidate’s first language, they must provide evidence before the beginning of the studentship that they meet the University minimum English Language requirements (IELTS 6.0 with at least 5.5 in each element).
Application Process
- Please review your academic eligibility as per link above.
- Once you have confirmed your academic eligibility, contact the supervisor by email as linked under your chosen project to discuss your interest and suitability for the project. Please ensure you include the following information for an informed discussion: title of project as listed, your academic qualifications, your occupation, brief outline of your interest and why you wish to undertake this PhD.
- If the supervisor supports your application, they will direct you to complete the full application form.
Available Projects:
Evaluating brain blood flow MRI to accelerate diagnosis of dementias
Brain health critically depends on high levels of tissue perfusion requiring tight control of cerebral blood flow (CBF). Arterial Spin Labelling (ASL) is a safe and fast way to measure CBF using MRI, making it an ideal adjunct diagnostic as part of routine clinical imaging for patients with neurological symptoms. With almost no perceived difference to the patient, ASL could be used to enhance the diagnostic specificity above conventional anatomical MRI for dementias. For example, for Alzheimer’s disease ASL MRI has been shown to have comparable sensitivity and specificity to gold-standard FDG-PET in research studies. Unlike MRI which is available nationwide, PET facilities are limited and not routinely available to most patients as part of a dementia diagnosis pathway.
In the past challenges for the translation of ASL MRI in the clinical include limited robustness, standardisation and accuracy; largely preventing its clinical diagnostic application in individual patients. This project will evaluate a new quantification and reporting tool based on algorithms developed in the Sir Peter Mansfield imaging Centre at the University of Nottingham and currently being prepared for regulatory approval by Quantified Imaging. This evaluation would initially involve retrospective analysis of data from international imaging studies, with the aim being to set up a prospective evaluation with patients in the local hospital trust. This would provide opportunities to engage patients and carers in study design and evaluation of potential changes to patient journey that novel imaging technology might bring about.
Reducing pain reactivity for children in the Emergency Department using non-invasive vagal nerve stimulation
Fractures in children are exceptionally common, with 1 in 3 children breaking a bone in their arm or leg by their 17th birthday. Children who sustain an injury and may be waiting in A&E for several hours before a diagnosis is made. There is a 33% rate of post-traumatic stress symptoms with children following limb fractures.
Vagal nerve stimulation has been shown to reduce feelings of anxiety in adults. Transcutaneous vagal nerve stimulation has been used safely for epilepsy, depression and anxiety disorders. However, traditional nerve stimulators use the application of direct electrical current, which can be painful and is therefore not suitable for use in children.
Low-intensity ultrasound has been shown to be effective in achieving similar levels of vagal nerve stimulation as traditional electrical stimulation.
This project will evaluate the use of an ultrasound vagus nerve stimulator (www.zenbud.health ) to reduce anxiety in children. While the benefits of this vagal nerve stimulation have been demonstrated in adults, there is very little evidence to demonstrate its effectiveness in children who may benefit significantly from the anti-anxiety effects during significantly stressful events and in reducing long-term mental health outcomes.
Improving clinical diagnosis of Dementia with Lewy Bodies in NHS memory assessment services
Dr Anto Praveen Rajkumar Rajamani
Neuropathological studies found Lewy bodies in 15-25% of post-mortem dementia brains. However, Dementia with Lewy bodies (DLB) is grossly underdiagnosed (<2%) by many NHS memory assessment services (MAS). Misdiagnosing DLB as Alzheimer’s Disease (AD) and prescribing an antipsychotic for managing associated neuropsychiatric symptoms may lead to life-threatening adverse effects. Hence, we aim to identify the barriers for DLB diagnosis, develop a comprehensive quality improvement (QI) plan, and to assess its feasibility in MAS.
Methodology:
- Systematic review of all screening and assessment instruments evaluating the four core clinical domains of DLB.
- Developing a natural language processing algorithm for extracting DLB diagnoses from anonymised Clinical Record Interactive Search system (CRIS) records.
- Focus group interviews with people with DLB, caregivers, MAS staff, and volunteers from the Lewy Body Society.
- Developing a comprehensive multidisciplinary quality improvement (QI) plan with the support of the Nottinghamshire healthcare NHS trust QI team.
- Modified Delphi consensus study to refine the QI plan.
- Assessing feasibility, acceptability and clinical impact of the QI plan in MAS.
Anticipated Impact:
This project will address an urgent clinical need. Improving clinical diagnosis and management of DLB has far reaching clinical, social and economic impact.
Understanding Opioid exposure, neurodevelopment, and mental health in children with pain
Many children experience severe acute or chronic pain due to fractures (50%) or conditions like scoliosis (3%). Opioids, while often necessary for pain relief, may disrupt neurodevelopment during critical windows, increasing risks of emotional dysregulation, anxiety, and altered pain processing.
This mixed-methods PhD will:
- Identify links between pain, opioid exposure, and adverse health outcomes.
- Explore the association between neural maladaptations and adverse health outcomes.
- Utilise functional MRI, pain, and psychological measures in a unique pilot study co-designed with children and families who have lived experience.
The project offers a rare opportunity to shape future guidelines for paediatric opioid use and early intervention strategies. You will be located within several centres for excellence (Pain Centre Versus Arthritis, Sir Peter Mansfield Imaging Centre, Biomedical Research Centre Themes Musculoskeletal, Surgery, Inflammation, and Recovery, and Magnetic Resonance and Precision Imaging themes).
This PhD is ideal for healthcare professionals with an interest in pain, child health, neurodevelopment, or mental health.
Leveraging population-level data for precision image-derived phenotyping in mental health
Despite the emergence of sophisticated tools over the last decade, from genomic profiling and high-resolution neuroimaging to AI models, the search for objective diagnostic tests and personalised treatments has so far come short for nearly every psychiatric disorder. These efforts have been fundamentally limited by a lack of understanding of how symptoms in mental illnesses map onto disrupted brain circuits. Magnetic Resonance Imaging (MRI) allows unique opportunities to provide insight into this question.
However, a key limitation in these explorations is routed in the indirect nature of MRI measurements. In particular, MRI-derived features reflect a range of nuisance factors that are not linked to biological variability. Hardware/software differences between MRI scanners can yield between-scanner variability for the same individual, which can be as large as, or in some cases exceed, between-subject variability and biological effects of interest. This lack of harmonisation between MRI scanners limits robustness and reproducibility of imaging-derived findings.
In this project we will develop a novel solution to this challenge, using unique pre-existing data that our team has built from a leading travelling-heads study. This will allow us to link population-level epidemiological data from the UK Biobank (comprising of 100,000 participants, recruited over ten years and each scanned with 5 imaging modalities), with targeted studies (that are smaller in cohort size, e.g. less than 100 participants, but likely considerably richer with respect to clinical phenotyping). We will subsequently leverage these capabilities to explore brain-symptom associations at scale in mental health cohorts by linking multiple imaging studies.
The project will provide hands-on experience and interdisciplinary, transferable skills relevant to a wide range of research topics, including: acquisition/processing of multi-modal neuroimaging data, handling large datasets, harmonising MRI data obtained from different clinical/research scanners, scripting/programming for scientific computing, data science and analytics for biomarker discovery, neuroimaging in mental illness. Healthcare professionals will be ultimately empowered to apply such research and analysis skills in practice and translate data into patient benefit.