Applications are invited for the post of Postdoctoral Research Associate in the Department of Computer Science, funded by Innovate UK. The project builds the collaboration between DDM Health Ltd., Coventry, and Department of Computer Science, Royal Holloway, University of London.

Cardiometabolic diseases, including type 2 diabetes, hypertension, dyslipidaemia, heart failure and stroke, are among the leading causes of illness and early death. They are strongly driven by high rates of overweight and obesity: in 2022, 64% of adults in England were overweight or living with obesity, and 29% were living with obesity. Excess weight is a major risk factor for type 2 diabetes and for the heart and circulatory complications that follow. Around 5.6 million people in the UK are living with diabetes, about 90% of whom have type 2 diabetes. High blood pressure is widespread: around 30% of adults in England have hypertension, and one in three - approximately 4.2 million people, are undiagnosed. Many people with serious cardiometabolic risk factors are therefore only identified when they present with a heart attack, stroke or other emergency.

Current risk assessment relies on clinic visits, blood tests and questionnaires. These tools work, but they are resource intensive, depend on people attending appointments and are not designed for low-burden, remote screening at scale. There are currently no tools in routine NHS use that analyse a person’s voice to help identify cardiometabolic risk.

This partnership aims to conduct voice-based risk detection for cardiometabolic disease with associated neurodegenerative conditions. It aims to develop novel deep learning algorithms, audio and vision transformers, and hybrid attention mechanisms, to detect dementia, Parkinson's disease (PD), diabetes, hypertension, heart failure, and stroke, by analysing voice patterns. Weakly supervised and zero/few-shot learning methods will also be exploited to classify unseen conditions without or with limited training samples, in order to tackle data sparsity issues.

Applicants should have the equivalent of a PhD degree from Computer Science with research expertise in machine learning, deep learning, audio/image/video classification, attention mechanisms, zero/few shot learning, and evolutionary algorithms. The Research Associate should have proficient programming skills in Python, MATLAB, and C++/Java. He/she should have good oral communication and academic writing skills. Relevant publication records would be advantageous. Applications from those who require a visa to work in the UK are welcomed. 

In return we offer a highly competitive rewards and benefits package including:

The post is based in Egham, Surrey where the University is situated in a beautiful, leafy campus near to Windsor Great Park and within commuting distance from London. 

The Research Associate is also expected to attend project meetings and training events in DDM Health Ltd. (Coventry). 

For an informal discussion about the post, please contact Professor Li Zhang, on li.zhang@rhul.ac.uk. Applicants are encouraged to send their CVs, abstract, outline of dissertations, and publications for any informal discussion. 

For queries on the application process, the Human Resources Department can be contacted by email at: recruitment@rhul.ac.uk 

Please quote the reference: 0626-212

Closing Date: 23:59, 26 July 2026  

Interview Date: Friday 31 July 2026

Closing Date: 26 Jul 2026

Section: Research

Salary: £41,374 to £48,639 per annum - including London Allowance

Applications are invited for the post of Research Assistant in the Department of Computer Science.

This is a fixed term post for one year from August 2026 (flexible start date).

The project seeks to advance the use of wearable technologies, such as smartwatches, earphones, and other body-worn devices, for continuous and non-invasive health monitoring. Through the development of robust machine learning models, and uncertainty-aware prediction methods, the project aims to improve the early detection of movement-related symptoms, behavioural changes and other digital biomarkers associated with neurological and long-term health conditions. The post holder will contribute to research outputs and support the wider development of digital health research within the Computer Science department.

The successful candidate will:

You will have a Master’s degree in Digital Health, Healthcare, or a closely related discipline, with a strong interest in applying machine learning to health and wearable sensor data. Experience in machine learning, data analysis, wearable sensing, digital biomarkers, or healthcare-related research would be highly desirable. 

The role offers an excellent opportunity to work on applied machine learning research with real-world health impact, contributing to the development of reliable computational methods for detecting, monitoring and helping to prevent health problems such as Parkinson’s disease. 

The successful candidate will join a vibrant and supportive research environment within the Department of Computer Science at Royal Holloway, University of London, and will contribute to the growing area of digital health and trustworthy AI. You will be expected to conduct both independent and collaborative research, develop and evaluate machine learning models for wearable sensor data, contribute to publications in leading academic venues, collaborate with PhD students and other members of the research team, and support the overall goals of the project in wearable sensing and uncertainty-aware healthcare AI.

In return we offer a highly competitive rewards and benefits package including:

• Generous annual leave entitlement 
• Rich Training and Development programmes
• Pension Scheme with generous employer contribution 
• Various schemes including Cycle to Work, Season Ticket Loans and help with the cost of Eyesight testing. 
• Free parking 

The post is based in Egham, Surrey where the University is situated in a beautiful, leafy campus near to Windsor Great Park and within commuting distance from London. 

For an informal discussion about the post, please contact Dr Khuong An Nguyen at Khuong.Nguyen@rhul.ac.uk

For queries on the application process the Human Resources Department can be contacted by email at: recruitment@rhul.ac.uk

Please quote the reference: 0626-201

Closing Date: 23:59, 3 July 2026  

Interview Date: To be confirmed

Closing Date: 03 Jul 2026

Section: Research

Salary: £34,670 to £40,284 per annum pro rata - including London Allowance