Research Fellow in Machine Learning for Remote Measurement Technology Data

at  Kings College London

Job id: 098907. Salary: £51,974 - £61,021 per annum inclusive of London Weighting Allowance.
Posted: 05 November 2024. Closing date: 01 December 2024.
Business unit: IoPPN. Department: Biostatistics & Health Informatics.
Contact details: Ewan Carr / Nicholas Cummins. ewan.carr@kcl.ac.uk / nick.cummins@kcl.ac.uk
Location: Denmark Hill Campus. Category: Research.
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About Us
King’s College London is an internationally renowned university delivering exceptional education and world-leading research. The university is dedicated to driving positive and sustainable change in society and realising our vision of making the world a better place. The Strategic Vision 2029 looks forward to King’s College London’s 200th anniversary in 2029 and sets out ambitious plans in five key areas:
Educating the next generation of change-makers
Challenging ideas and driving change through research
Giving back to society through meaningful service
Working with our local communities in London
Fostering global citizens with an international perspective
The Institute of Psychiatry, Psychology & Neuroscience (IoPPN) is a Faculty of King’s College London and the largest academic community in Europe devoted to the study and prevention of mental illness and brain disease.
The School of Mental Health & Psychological Sciences (MHaPS) is one of three schools under the IoPPN, comprised of the four departments of Psychology; Social, Genetic & Developmental Psychiatry; Health Service & Population Research; and Biostatistics & Health Informatics. With over 100 Principal Investigators, our research spans development from childhood to old age, encompassing basic research through to the development and implementation of treatments, services and policy.
We are looking for a highly skilled and motivated Postdoctoral Research. Our department conducts cutting-edge methodological and applied collaborative research, contributing to the Institute’s wide-ranging interdisciplinary research programme.
About The Role
We are seeking a highly skilled and motivated Postdoctoral Research Associate to join our dynamic team applying remote measurement technologies (RMT) to prediction of mental health outcomes. Bringing your expertise in prediction modelling and machine learning, you will develop advanced multimodal prediction models using RMT, such as smartphones and wearables, to forecast outcomes in depression.
The postholder will use information from the RADAR-CNS study (https://www.radar-cns.org/) and related datasets to develop and validate multivariable models to predict symptom severity and relapse in Major Depressive Disorder (MDD) using regression-based techniques (e.g., LASSO or Elastic Net). They will then use contemporary machine learning models (such as self-supervised learning) to quantify the disturbance in multivariate passive data as a precursor for relapse or symptom-worsening and compare this approach with conventional models. They will be expected to prepare their research findings for dissemination in high-impact, REF-returnable journals and discuss their research with Patient and Public Involvement and Engagement (PPI-E) groups.
The successful candidate will have a strong background in prediction modelling and machine learning, be passionate about using RMT data in clinical settings to enhance our understanding of depression. They will contribute to a broader research programme that includes multimodal signal processing and analytics, with a focus on machine learning within the health data domain.
To be considered for this role, you should have:
A PhD (or equivalent) in a relevant discipline (e.g., biostatistics, machine learning, computer science, clinical informatics, natural language processing).
Strong skills in data analysis and programming (e.g., Python or R).
A track record of publications in high quality scientific journals.
Experience or interest in self-supervised learning and foundation models is an advantage.
The postholder will report to Dr Ewan Carr and Dr Nicholas Cummins, and also be accountable to Professor Mathew Hotopf and Dr Srinivasan Vairavan.
This is a full-time post, and you will be offered a fixed term contract of 2-years
About You
To be successful in this role, we are looking for candidates to have the following skills and experience:

Essential criteria

• PhD or equivalent in a relevant discipline
• Experience in prediction modelling and machine learning techniques
• Proficiency in data analysis and programming with languages such as Python or R.
• Proven track record of writing and publishing scientific papers
• Experience with multivariable regression approaches
• Familiarity with self-supervised machine learning methods

Desirable criteria

• Experience with remote measurement technologies (RMT), such as smartphones or wearable devices, in research applications
• Understanding of depression and mental health outcomes, particularly in relation to predictive modeling
• Experience in grant writing and securing research funding

Downloading a copy of our Job Description
Full 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 “

Please refer the Job description for details