Post-doctoral Research Associate in Machine Learning for Mental Health

Post-doctoral Research Associate in Machine Learning for Mental HealthDepartment : Res Dept of Biomedical ComputingAbout UsThe applicant will work as part of a collaborative team, led by Dr Sarah Morgan at the School of Biomedical Engineering and Imaging Sciences.

Want to apply Read all the information about this position below, then hit the apply button.

The School is a world leading centre of expertise in AI for healthcare, providing an outstanding environment in which to develop machine learning tools and engage with an interdisciplinary community of researchers with an interest in AI for healthcare.

The post holder will have opportunities to learn from colleagues across the department through regular seminars and tutorials, and benefit from close links to industry through the London Institute for Healthcare Engineering.

They will also collaborate closely with researchers and clinicians at the King’s IoPPN, which is a world leading centre for Psychiatric research. About The RoleThis is a 2 year postdoctoral research post, with the possibility of extension to 4 years, working on the UKRI funded project ‘PROSPECT: Predicting psychosis outcomes from speech and brain connectivity’.

The overall aim of the role is to develop innovative machine learning approaches to predict longitudinal symptom changes for patients with psychotic illnesses, using patterns of brain connectivity derived from MRI.The post holder will work at the intersection of machine learning, neuroimaging and Psychiatry, developing methods with the potential to improve our ability to predict clinical outcomes.

They will have the opportunity to work with rich brain MRI datasets from patients with psychotic illnesses, curate and process these datasets, and derive both functional and structural brain networks.

This will include using our group’s new Morphometric Inverse Divergence (MIND) approach for estimating structural similarity networks, which enables robust structural brain networks to be derived from T1-weighted images alone and has already been shown to be sensitive to schizophrenia.A key focus of the project will be on testing novel approaches to improve the accuracy of psychosis outcome prediction from structural and functional brain networks.

To that end, we will explore uncertainty-aware machine learning approaches, to assess whether prediction accuracy can be improved by focussing on particular subsets of patients.

We will also investigate different ways to define longitudinal outcomes, and test whether combining functional and structural networks in novel ways can help improve prediction accuracy.

For example, we will explore whether functional networks generated from structural brain networks can be used to capture additional predictive power.

If the role holder desires, there may also be scope to relate brain imaging to speech and language data.About YouTo be successful in this role, we are looking for candidates to have the following skills and experience:Essential criteriaPhD (or near completion) in a relevant subject area (including Computer Science, Engineering, Physics, Mathematics, Psychiatry, Psychology or Neuroscience.

Note that this list is not exhaustive- candidates from other relevant backgrounds are welcome to apply); Experience working with brain MRI data; Excellent computational skills, including experience with Machine Learning (e.g.

sklearn, pytorch, tensorflow in Python), and the ability to learn new computational techniques quickly as required; Experience writing up research results for publication; Able to communicate technical results to colleagues, and other researchers at seminars/conferences; Strong organisational and time management skills; Able to work independently, using initiative and creativity to explore own research directions; Able to work collaboratively as part of a cohesive team; Continuously updates knowledge in the specialist area and engages in continuous professional development. Desirable criteriaExperience of network analysis/graph theoretical techniques; Experience working with data related to mental health. Downloading a copy of our Job DescriptionFull details of the role and the skills, knowledge and experience required can be found in the Job Description document, provided at the bottom of the next page after you click “Apply Now”.

This document will provide information of what criteria will be assessed at each stage of the recruitment process.Further InformationWe pride ourselves on being inclusive and welcoming.

We embrace diversity and want everyone to feel that they belong and are connected to others in our community.We are committed to working with our staff and unions on these and other issues, to continue to support our people and to develop a diverse and inclusive culture at King's.As part of this commitment to equality, diversity and inclusion and through this appointment process, it is our aim to develop candidate pools that include applicants from all backgrounds and communities.We ask all candidates to submit a copy of their CV, and a supporting statement, detailing how they meet the essential criteria listed in the advert.

If we receive a strong field of candidates, we may use the desirable criteria to choose our final shortlist, so please include your evidence against these where possible.To find out how our managers will review your application, please take a look at our ‘ How we Recruit’ pages. Interviews are expected to be held in November.We are able to offer sponsorship for candidates who do not currently possess the right to work in the UK.Grade and Salary : £45,031

• £48,607 per annum inclusive of London Weighting AllowanceJob ID : 159026Post Date : 21-Sep-2026Close Date : 18-Oct-2026Contact Person : Dr Sarah MorganContact Details :