Publisher
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King's College London

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PhD Studentship Pathway in Predicting OCD Onset Using Machine Learning King's College London in United Kingdom

Degree Level

PhD

Field of study

Computer Science

Funding

The role is a full-time, five-year Grade 5 salaried position paying £39,755 per annum including London Weighting Allowance, with a fixed-term contract until 30 April 2031. The associated PhD studentship covers UK Home tuition fees only; candidates liable for Overseas tuition fees must cover the difference between Home and Overseas rates.

Deadline

Nov 16, 2026

Country flag

Country

United Kingdom

University

King's College London

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Keywords

Computer Science
Psychology
Psychopathology
Neuropsychology
Medical Science
Survival Analysis
Child And Adolescent Psychiatry
Statistics
ML

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About this position

King’s College London is offering a research assistant position with a pathway into doctoral research on predicting the onset of obsessive-compulsive disorder (OCD) in children and young people. The project is based in the Department of Child and Adolescent Psychiatry within the Institute of Psychiatry, Psychology & Neuroscience and is connected to the King’s Maudsley Partnership for Children and Young People.

Supported by a Wellcome Trust-funded programme, the doctoral research will bring together clinical and behavioural information from nine longitudinal cohorts across the United Kingdom, Brazil and Sweden, representing approximately 90,000 participants. The student will contribute to harmonizing multimodal datasets that include behavioural tasks, self-report measures and clinical assessments, while evaluating data provenance and quality across cohorts.

A central research objective is the development of machine-learning survival models that can predict OCD onset. The project offers experience in predictive modelling, survival analysis, clinical data science and developmental psychopathology, with a focus on early detection and prevention of neuropsychiatric disorders. The work combines computational methods with youth mental health research and international, multi-cohort collaboration.

The successful candidate will work closely with Professors Shaw and Gustavo Sudre and collaborate within a multidisciplinary research environment linking academic research with NHS implementation. The role is designed for someone interested in developing an academic career in neuroscience, psychology, psychiatry, clinical research or computational mental health.

Funding details

The role is a full-time, five-year Grade 5 salaried position paying £39,755 per annum including London Weighting Allowance, with a fixed-term contract until 30 April 2031. The associated PhD studentship covers UK Home tuition fees only; candidates liable for Overseas tuition fees must cover the difference between Home and Overseas rates.

What's required

Applicants must have an MSc with Distinction or Merit in neuroscience, psychology or a closely related discipline; direct experience collecting clinical research data from young people, including structured interviews, questionnaires or behavioural tasks; experience integrating and cleaning multimodal behavioural, self-report and clinical datasets; demonstrated ability to independently design and implement machine-learning pipelines on psychiatric or clinical data; Python and/or R programming experience for data processing and statistical modelling, with willingness to develop survival-analysis and harmonization skills; ability to work effectively in multidisciplinary teams; excellent organization and attention to data quality and documentation; and strong scientific communication skills demonstrated through presentations or manuscript contributions. Desirable experience includes a publication or submitted manuscript, neuroimaging or genomic data, OCD or anxiety research in young people, a Good Clinical Practice certificate or willingness to obtain one within three months, and awareness of data governance and GDPR requirements.

How to apply

Apply through the King's College London vacancy page. Submit a CV and supporting statement explaining how you meet the essential criteria and, where possible, the desirable criteria. Apply by 16 November 2026; the advert may close early.

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