PhD Studentship: Developing digital tools to support a personalised preventative pathway for childrens mental health

Job title:

PhD Studentship: Developing digital tools to support a personalised preventative pathway for childrens mental health

Company

University of Cambridge

Job description

Based within the Timely Research Group, Department of Psychiatry, University of CambridgeA full scholarship funded through Peterhouse, University of CambridgeThe Department of Psychiatry, University of Cambridge, is pleased to announce a Studentship to conduct research for a doctoral degree to start in April 2025 for a duration of three years. The PhD student will be a part of the UKRI Future Leaders Fellowship project (PI: Moore) Transforming child mental health: co-designing, building and evaluating digitally enabled, personalised, prevention pathways. Professor John Suckling will co-supervise. The Studentship is suitable for candidates with a good degree in engineering, computer science, genetics, mathematics, psychology, biology, medicine, neuroscience, mathematics, biochemistry, or a related discipline. We particularly welcome applications from people from backgrounds that are under-represented in academic research.The studentship will be hosted within the Timely Research Group, Department of Psychiatry. The Department has an outstanding international reputation in research, rated the best psychiatry department in the UK and in Europe, and has excelled in the last three Research Assessment Exercises. The University of Cambridge is consistently ranked among the very top universities in the world.The Timely project aims to develop digitally supported personalised prevention pathways for children’s mental health services. Baseline work has been carried out to construct a linked, population-level, multi-agency, longitudinal database including administrative and clinical records from health, education and social care records. A blueprint for a preventative pathway has been developed. This project will take forward the blueprint, refine it with a broad range of stakeholders including children and families, and co-develop detailed specifications for AI-driven digital tools. Particular attention will be placed on taking a responsible AI approach.We are particularly interested in candidates who would like to use large longitudinal datasets to investigate how heterogeneous factors contribute to differences in neurodevelopmental and mental health conditions. As a part of the PhD, candidates will build complex longitudinal models to investigate the role of a range of factors, investigating their correlation and interaction. This knowledge will be used to develop responsible AI tools and validate them, with particular attention to ensuring they are equitable and do not exacerbate or create bias in the delivery of care. Candidates will develop skills to handle large-scale datasets, longitudinal modelling, handling electronic heath records, and develop their knowledge of AI and machine learning. Candidates are asked to submit a potential project title and a research proposal within this research area.To apply, please email the following documents:

• An academic CV.
• A research proposal within this research area (maximum 2000 words, excluding bibliography or figures).
• A cover letter indicating a brief summary of your research interests, any completed research conducted, interests and skills in statistical methods, analyses of large datasets, and coding, and a clear statement of your eligibility for this funding award.

Applications must be received by midnight on 30 November 2024. Interviews will be held within a month of the application deadline.Please quote reference RN43277 on your application and in any correspondence about this vacancy.The University actively supports equality, diversity and inclusion and encourages applications from all sections of society.The University has a responsibility to ensure that all employees are eligible to live and work in the UK.

Expected salary

Location

Cambridge

Job date

Wed, 18 Sep 2024 00:09:05 GMT

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