| Project Supervisor | Mariana Pinto da Costa |
| Institution & Department | King’s College London – Department of Psychological Medicine, Institute of Psychiatry, Psychology & Neuroscience |
| Research Area | RA1: Global Health Innovation |
| Project Start Date | End of June 2026 onwards – flexible start date offered. |
| Project Duration | 3 months |
| Application Deadline | 4th June 2026 |
| Working Pattern | Either full-time or part-time. Please discuss and agree on Working Patterns with the Project Supervisor. |
| Working Arrangements | Hybrid |
| Flexible (can be undertaken fully remote or predominantly remote, or with greater in-person attendance depending on the candidate’s preferences). | |
| How to Apply | View Guidance Here |
Project Description
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Psychosis is a serious mental disorder that can greatly affect how a person thinks, feels, and behaves. A particularly challenging aspect of psychosis is the presence of negative symptoms, such as reduced motivation and social withdrawal, which are often resistant to treatment. These symptoms frequently contribute to social isolation and loneliness. Loneliness is increasingly recognised as a major public health concern, associated with worse mental and physical health outcomes, increased service use, and slow down their recovery
Mental health research is being transformed by the very large data resources accruing from electronic health records, amongst other sources, creating exciting emerging capabilities and a rapidly evolving discipline of Mental Health Data Science. At the NIHR Maudsley Biomedical Research Centre, and particularly through our Clinical Record Interactive Search (CRIS) platform, we have created internationally leading data resources which contain unparalleled granularity (detail) of information on large clinical populations receiving routine care, including the application of natural language processing to extract structured data from text fields.
Making use of these resources, quantitative methodology will be used to investigate the link between recorded loneliness in patients with psychosis, clinical phenotypes (negative symptoms, positive symptoms, depressive symptoms, and manic symptoms) and patient characteristics, such as age and ethnicity, as well as the patterns of healthcare use, health care efficiency, and clinical outcomes.
This project will contribute to the growing field of mental health data science by providing novel insights into how loneliness is documented in routine clinical care and how it relates to outcomes in psychosis. Findings may inform future interventions and service development aimed at reducing loneliness and improving recovery in this population.
Internship Details
The internship will take place within a supportive and collaborative research environment at King’s College London. The student will engage with a multidisciplinary team, including clinicians, data scientists, and researchers.
Internship Structure
The student will undertake a structured programme of research activities, including:
1) becoming familiar with the CRIS dataset and relevant variables,
2) contributing to data cleaning, preparation, and variable construction,
3) leading quantitative analyses,
4) applying multivariate statistical methods (e.g., regression modelling) and
5) interpreting findings in collaboration with the supervisory team.
The intended outputs include:
1) A structured analytical report summarising findings,
2) Draft an academic manuscript,
3) Presentation of findings at a departmental or research group seminar or conference.
The student will be supported to contribute to a peer-reviewed publication.
Anticipated Benefits for the Student
This internship will provide comprehensive doctoral-level training, equipping the student with both advanced research competencies and highly transferable professional skills essential for a career in population health, or data-driven mental health research.
The student will develop expertise in working with large-scale electronic health record (EHR) data within a real-world clinical research environment. This will include:
- Handling complex, high-dimensional datasets – gaining hands-on experience with the CRIS platform, including data extraction, linkage, governance, and secure data handling procedures.
- Data management and preparation – developing robust skills in data cleaning, variable construction, and reproducible research practices, ensuring high-quality analytical pipelines.
- Advanced quantitative analysis – applying and critically engaging with statistical techniques appropriate for observational health data (e.g., multivariate regression models, longitudinal analyses, and handling confounding and bias).
- Interpretation of complex clinical data – translating statistical outputs into clinically meaningful insights, with attention to limitations, and implications for mental health services and policy.
- Methodological rigour – strengthening understanding of epidemiological principles, including study design, and the challenges inherent in routinely collected health data.
Alongside technical expertise, the internship will foster a broad range of transferable skills aligned with doctoral-level training:
- Critical appraisal and synthesis of literature – engaging with current research to contextualise findings, identify gaps, and inform analytic decisions.
- Scientific writing and dissemination – contributing to the preparation of an academic manuscript, and developing skills in structuring and communicating complex findings clearly and effectively.
- Presentation and communication skills – presenting research progress and findings to multidisciplinary audiences, enhancing the ability to tailor communication to both specialist and non-specialist stakeholders.
- Collaborative working – operating within a multidisciplinary team, including clinicians, statisticians, and health data scientists, and developing skills in teamwork, feedback integration, and project coordination.
- Research independence and project management – gaining experience in planning and executing components of a research project, managing timelines, and taking increasing ownership of analytical work.
The student will benefit from immersion in the NIHR Maudsley Biomedical Research Centre environment, gaining insight into the translation of research into clinical practice and policy. Exposure to established researchers and ongoing projects will support networking, mentorship, and future career opportunities.
Skills, Experience and Knowledge Requirements
Essential Requirements:
- Basic knowledge of quantitative research methods
- Experience with statistical software (e.g., R, Stata, SPSS, or Python)
Desirable Requirements:
- Experience analysing large datasets
- Familiarity with epidemiological research methods