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An Artificial Intelligence approach for epidemiological samples and their implementation in mental healthcare systems

Start date

1st October 2024


1 fully funded 3-year PhD studentship, Full-Time, Department of Psychosis Studies, IoPPN.


Artificial Intelligence holds tremendous potential to revolutionise decision-making in mental healthcare systems. By analysing vast amounts of patient data and identifying patterns, AI algorithms can assist clinicians in accurate diagnosis, personalised treatment plans, and timely interventions. AI-powered tools can also monitor patient progress, provide real-time feedback, and offer support to both patients and healthcare providers. Indeed, such applications have been achieved in other areas of medicine. Data from medical records have been used to identify individuals at risk of intensive care use, imaging data have shown to enhance detection of diminutive adenomas and hyperplastic polyps and treatment response prediction to fifteen distinct cancer types has been achieved using advanced machine learning approaches. Such advancements applied in mental healthcare systems hold promise to enhance the quality and accessibility of services, leading to improved outcomes and a better understanding of mental illnesses.

While AI algorithms have shown remarkable potential in psychiatric research, their full impact on healthcare remains limited without translation into clinical settings. Bridging this gap is crucial to unlock AI's benefits in patient care. This PhD project will utilise existing multimodal data (neuroimaging, clinical records, genomics, blood-based biomarkers) from the EU FP-7 funded PRONIA study, the UK Biobank, the German National Cohort (NAKO) as well as the Clinical Record Interactive Search (CRIS) system within the NIHR Maudsley Biomedical Research Centre. The student will be trained in novel machine and deep learning methods to build predictive models of mental health disorders as well as examine their applicability to real-world clinical data (CRIS) and epidemiological data (UK Biobank and NAKO).

The student will be based at the Artificial Intelligence in Mental Health (AIM) lab which is co-led by the Chair of Precision Psychiatry Professor Nikolaos Koutsouleris and the Lecturer in Artificial Intelligence in Mental Health Dr Paris Alexandros Lalousis. It is a new lab based in the Department of Psychosis Studies at the Institute of Psychiatry, Psychology & Neuroscience. The student will benefit from access to a strong network of collaborators and research innovation within the Institute of Psychiatry, Psychology & Neuroscience, the NIHR Maudsley Biomedical Research Centre, as well as the Early Psychosis Studies and the Section for Precision Psychiatry in Munich. This will provide a rich and diverse research environment, helping the student develop their skills and knowledge, and a strong professional network. Specific training will include data analysis using shallow and deep learning techniques and curation/harmonization of existing phenotypic data across the aforementioned cohorts; these are essential skills in mental health research and will therefore be valuable for the student's career development. The studentship will come with a 2- to 4-month residency in Munich for deep/machine learning training in Professor Nikolaos Koutsouleris’ lab.

We are looking for candidates who have strong interpersonal skills, a willingness to learn from and teach others, a desire to be an innovative leader in the field, and strong technical and analytical abilities.


1st Supervisor: Dr. Paris Alexandros Lalousis

2nd Supervisor: Professor Nikolaos Koutsouleris

Entry requirements

Applicants should have (or be expected to obtain) a Bachelors degree with 2:1 honours (or Overseas equivalent). A 2:2 degree may be considered only where applicants also offer a Masters with Merit.

Award types and eligibility

Students will be fully funded for three years full time, to include home tuition fees (studentship not available to Overseas applicants), annual stipend and some research and travel costs. Overseas applicants may apply but will need to cover the difference in fees.

To be treated as a Home student, candidates must meet one of the following criteria:

  • A UK national (meeting residency requirements)
  • Settled status
  • Pre-settled status (meeting residency requirements)
  • Indefinite leave to remain or enter

Further information

About the IoPPN (

Studying at the IoPPN (

Research degrees at the IoPPN (link to

AIM lab (link to

How to apply

Applicants must complete and submit an online admissions application, via the admissions portal by midnight (23:59 GMT) on 26th January 2024.

On the ‘Choosing a programme’ page, please select Psychosis Studies Research MPhil/PhD (Full Time).

In your application, you will be asked to include:

  • Academic Transcripts – where applicable, academic transcripts must be submitted with the online admissions application
  • Details of your qualifications (you will need to attach copies)
  • A personal statement describing your interests and why you wish to apply for this project. Please include this as an attachment rather than using the text box.
  • Academic References – all admissions applications require one supporting reference. If the applicant is relying on thier referees to submit a reference directly to the College after they have submitted thier admissions application, then the applicant must ensure that (1) their chosen referee is made aware of the funding deadline (i.e. 7 days from application deadline) and (2) that the reference needs to be sent from an institutional email address.

In the Funding section, please tick box 5 and include the following reference: PL-IOPPN-CRIS-NK-24

Please note there is no need to complete the Research Proposal section in your application as the project has already been set. You are welcome to email Dr Paris Alexandros Lalousis ( for more information regarding the project and studentship. 

If you have any queries regarding the application process, please contact the Education support team at

References must be received by the deadline for the applicant to be eligible. Only shortlisted applicants will be contacted.

Closing date

26th January 2024 (23:59 GMT)




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