PhD Studentship: Developing digital tools to support a personalised preventative pathway for children's mental health

University of Cambridge
Cambridge
1 year ago
Applications closed

Related Jobs

View all jobs

Machine Learning Researcher Statistics Python AI

Machine Learning Researcher Statistics Python AI - Client Server

Computer Vision Scientist - Darcie Talent

Machine Learning Researcher Statistics Python AI

Data Scientist Intern (PhD level)

Data Scientist Intern (PhD level)

Based within the Timely Research Group, Department of Psychiatry, University of Cambridge

A full scholarship funded through Peterhouse, University of Cambridge

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.

Applicants for the Studentships should have, or expect to gain a 1st class or a high 2.1 class Honours degree in a relevant discipline, and may also have completed further research training or a Master's degree. The stipend will be paid for the 3-year duration of the award. Only the fees for home students will be met in full. In addition, the Studentship includes modest funding for running costs of the research and costs for travel to scientific conferences.

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 onOctober 31st 2024. Interviews will be held within a month of the application deadline.

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.

Subscribe to Future Tech Insights for the latest jobs & insights, direct to your inbox.

By subscribing, you agree to our privacy policy and terms of service.

Industry Insights

Discover insightful articles, industry insights, expert tips, and curated resources.

The Skills Gap in AI Jobs: What Universities Aren’t Teaching

Artificial intelligence is no longer a future concept. It is already reshaping how businesses operate, how decisions are made, and how entire industries compete. From finance and healthcare to retail, manufacturing, defence, and climate science, AI is embedded in critical systems across the UK economy. Yet despite unprecedented demand for AI talent, employers continue to report severe recruitment challenges. Vacancies remain open for months. Salaries rise year on year. Candidates with impressive academic credentials often fail technical interviews. At the heart of this disconnect lies a growing and uncomfortable truth: Universities are not fully preparing graduates for real-world AI jobs. This article explores the AI skills gap in depth—what is missing from many university programmes, why the gap persists, what employers actually want, and how jobseekers can bridge the divide to build a successful career in artificial intelligence.

AI Jobs for Career Switchers in Their 30s, 40s & 50s (UK Reality Check)

Changing career into artificial intelligence in your 30s, 40s or 50s is no longer unusual in the UK. It is happening quietly every day across fintech, healthcare, retail, manufacturing, government & professional services. But it is also surrounded by hype, fear & misinformation. This article is a realistic, UK-specific guide for career switchers who want the truth about AI jobs: what roles genuinely exist, what skills employers actually hire for, how long retraining really takes & whether age is a barrier (spoiler: not in the way people think). If you are considering a move into AI but want facts rather than Silicon Valley fantasy, this is for you.

How to Write an AI Job Ad That Attracts the Right People

Artificial intelligence is now embedded across almost every sector of the UK economy. From fintech and healthcare to retail, defence and climate tech, organisations are competing for AI talent at an unprecedented pace. Yet despite the volume of AI job adverts online, many employers struggle to attract the right candidates. Roles are flooded with unsuitable applications, while highly capable AI professionals scroll past adverts that feel vague, inflated or disconnected from reality. In most cases, the issue isn’t a shortage of AI talent — it’s the quality of the job advert. Writing an effective AI job ad requires more care than traditional tech hiring. AI professionals are analytical, sceptical of hype and highly selective about where they apply. A poorly written advert doesn’t just fail to convert — it actively damages your credibility. This guide explains how to write an AI job ad that attracts the right people, filters out mismatches and positions your organisation as a serious employer in the AI space.