Research Associate, EDoN: Early Detection of Neurodegenerative Disease

Alan Turing Institute London United Kingdom Research Programmes
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Company Description

The Alan Turing Institute is the UK’s national institute for data science and artificial intelligence. The Institute is named in honour of the scientist Alan Turing and its mission is to make great leaps in data science and artificial intelligence research in order to change the world for the better.


The Alan Turing Institute has been awarded a grant by Alzheimer’s Research UK (ARUK) to lead the Analytics Hub for the EDoN initiative.

Early Detection of Neurodegenerative diseases (EDoN) is the largest initiative in the world that will collect, share and analyse clinical and digital health data to detect diseases like Alzheimer’s. Ultimately, this approach would be used by doctors to give an earlier and much more accurate diagnosis of dementia diseases.

The Alan Turing Institute is leading on the EDoN Analytics Hub which is tasked with designing and performing the analyses that will allow EDoN to make sense of the data collected in the project. The Analytics Hub is composed of data scientists and is responsible for developing, validating and refining machine learning ‘fingerprint’ models that can detect the diseases that cause dementia at their earliest stage.

The Health and Medical Sciences programme at the Turing delivers research into the theory and methods of AI, statistics, and data analytics underpinning medical and health applications that will enable scientists to do better science, without compromising respect for privacy and patient trust. The Analytics Hub is led by Professors Richard Everson (Alan Turing Institute and Exeter University) and Chris Holmes (Alan Turing Institute and Oxford University), and is recruiting two Senior Research Associate/Research Associates to support the data analytics and modelling. There may be opportunities for a joint appointment or visiting positions at the universities of Oxford or Exeter.


The Research Associate will work closely with Professors Richard Everson and Chris Holmes and the Analytics Hub to deliver the data analytics and modelling aspects of the Analytics Hub. The post-holder will explore existing and develop novel machine learning methods to model and analyse retrospective and prospective data collected by the EDoN project and held in the Turing Secure Research Environment.

You will develop novel methods to, for example, reduce misclassification of individuals due to co-morbidities, accurately predict particular disease subtypes, detect and model cognitive decline, combine multi-modal datasets. Initial work will be on retrospective data, but we will rapidly move to novel forms of data collected on low-burden digital platforms, such as smart phones and wearable technologies. Particular attention will be paid to issues around interpretability and reproducible research. You will produce breakthrough research in machine learning and data science for the early detection of neurodegenerative disease, publishing in top-rated journals and conferences.

Successful candidates should have a strong quantitative background and preferably a strong machine learning and statistics background and be eager to do research at the interface of AI and neuroscience. There is significant scope for the postholder to develop new skills and grow in the role. We are excited to work with applicants who bring a fresh perspective on a paradigm shifting and ambitious research goal.

Your colleagues in the Analytics Hub include data wranglers mentored by Dr Ann-Marie Mallon and a reproducibility lead mentored by Dr Kirstie Whitaker at The Alan Turing Institute. The EDoN Clinical Hub is led by Professor Zuzana Walker at University College London and the EDoN Digital Hub is led by Dr Chris Hinds at the Big Data Institute at the University of Oxford. Professor Zoe Kourtzi at the University of Cambridge is the Scientific Director of EDoN and the Chair of the EDoN Steering Group. More information about members of the EDoN collaboration can be found at htttps:// We expect that success in the role will also require close collaboration with other communities such as the Brain Imaging Data Structure (BIDS), UK Dementia Research Institute, and Deep Dementia Phenotyping Network, among others.

This programme of work sits under the Health and Medical Sciences Programme at the Alan Turing Institute and as such the Health Programme delivery team will enable extensive opportunities for you to collaborate with and learn from experts from across all programmes at The Alan Turing Institute. There may be opportunities for a joint appointment or visiting positions at the universities of Oxford or Exeter.


  • Design and implement cutting edge statistical and machine learning methods to detect cognitive decline and dementia-causing diseases. Demonstrate internally across the EDoN consortia and the broader health data science communities, how data science and AI methods can provide predictive modelling to help clinicians in the detection of dementia.
  • Compare the power of cognitive, neuroimaging and digital markers in retrospective and prospective cohorts to accurately detect dementia.
  • Determine the integrity of digital markers and estimating the scale of data collection that will be necessary for the EDoN project’s overarching vision of detecting diseases like Alzheimer’s years before the symptoms of dementia start.
  • Analyse initial prospective data from the Predictors of COgnitive DECline in attenders of memory clinic (CODEC) study based at the Essex Neurocognitive Clinic along with other pilot data to determine the integrity of digital markers and estimating the scale of data collection that will be necessary.
  • In collaboration with the EDoN Reproducibility Lead, deliver robust and transparent algorithms and models that can be reproducibly deployed at scale for future analysis work.
  • Catalyse connections and collaboration between EDoN team members across a distributed team. This could come in the form of synchronous regular meetings or it could occur asynchronously, for example through active engagement on distributed channels such as Slack and private GitHub repositories.
  • Communicate technical topics to colleagues and external partners by preparing and presenting reports, blog posts, organising and delivering presentations, and taking an active role in meetings and discussions. Communications may be synchronous or asynchronous, remote or in-person, and must be prepared at the appropriate granularity of detail for the audience.
  • Publish – as lead or co-author – peer-reviewed research articles and, if interested, perspective, opinion and commentary articles
  • Contribute to the research aims and challenges of the EDoN Initiative, The Alan Turing Institute’s Health and Medical Sciences Programme, and those of The Alan Turing Institute more broadly. This may be through active participation at in-person and online workshops or in conversation with experts across these overlapping communities.


  • A PhD (or equivalent experience and/or qualifications) in a relevant area, which will include Statistics, Mathematics, Engineering, Computer Science, or related discipline.
  • Strong background in one or more of the following areas: Bayesian inference, ensemble models, multivariate time-series analysis, medical image analysis.
  • Strong quantitative background, and preferably a strong machine learning and statistics background
  • Experience managing, structuring and analysing research data.
  • An understanding of the importance of good practices for producing reliable software and reproducible analyses (e.g., version control, issue tracking, automated testing, package management, literate analysis tools such as Jupyter and Rmarkdown.
  • Excellent written and verbal communication skills, including experience in the visual representation of quantitative data, the ability to write for publication, present research proposals and results, and represent the research group at meetings.
  • Ability to collaborate successfully in a multidisciplinary environment across different levels of seniority.
  • Experience and ability of developing reports, ensuring conceptual relevance, comprehensiveness, and currency of information.

Please see the job description attached for a full breakdown of the duties, responsibilities and person specification.

Other information


If you are interested in this opportunity, please click the apply button below. You will need to register on the applicant portal and complete the application form including your CV and covering letter. If you have questions about the role or would like to apply using a different format, please contact us on 020 3970 2148 or 0203 862 3340, or email [email protected].

If you are applying for more than one role at the Turing, please note that only one Cover Letter can be visible on your profile at one time. If you wish to apply for multiple roles and do not want to overwrite your existing Cover Letter, please apply for the role using the button below and forward your additional cover letter directly to [email protected] quoting the job title.

CLOSING DATE FOR APPLICATIONS: Sunday 06 November 2022 at 23:59

We reserve the right to close this vacancy early or to interview suitable candidates before the closing date if enough applications are received.


This full time post is offered on a fixed term basis for 2 years. The annual salary is £38,850 - £46,200 plus excellent benefits, including flexible working and family friendly policies,

Candidates who have not yet been officially awarded their PhD will be appointed as Research Assistant at a salary of £36,235.

We are currently assessing the results of our hybrid working trial, which ran for six months. We will soon publish a long-term workplace policy: as a guide, we anticipate this will be between 2-4 days per month. Some roles may require the jobholder to spend a greater number of days in the office, but the hiring manager will be able to confirm this during the interview


The Alan Turing Institute is committed to creating an environment where diversity is valued and everyone is treated fairly. In accordance with the Equality Act, we welcome applications from anyone who meets the specific criteria of the post regardless of age, disability, ethnicity, gender reassignment, marital or civil partnership status, pregnancy and maternity, religion or belief, sex and sexual orientation.

We are committed to building a diverse community and would like our leadership teal to reflect this. We therefore welcome applications from the broadest spectrum of backgrounds.

Reasonable adjustments to the interview process will be made for any candidates with a disability.

Please note all offers of employment are subject to obtaining and retaining the right to work in the UK and satisfactory pre-employment security screening which includes a DBS Check.

Full details on the pre-employment screening process can be requested from [email protected].