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Knowledge Transfer Partnership (KTP) Associate

Department of Computer Science

Location
Other

Salary
£40,839 to £48,003 per annum - including London Allowance

Post Type
Full Time

Hours per Week
35

Weeks per Year
52

Closing Date
23.59 hours BST on Thursday 05 June 2025

Reference
0425-090

Right to work: Applications from job seekers who require sponsorship to work in the UK are welcome and will be considered alongside all other applications. For further information visit the UK Visas and Immigration website

Full-Time, Fixed-Term (24 months)

Applications are invited for the post of a Knowledge Transfer Partnership (KTP) Associate in the Department of Computer Science. The project is funded by Innovate UK and DDM Health Ltd. DDM Health specialises in digital therapeutics and virtual care services with a focus on cardiometabolic health leveraging advanced technology to manage and prevent chronic conditions. This KTP project builds the collaboration between DDM Health Ltd., Coventry, and Department of Computer Science and Department of Biological Sciences, Royal Holloway, University of London.

The project aims to develop an innovative AI-driven tool for diabetes and prediabetes detection using voice inputs. Currently, Type 2 diabetes (T2D) is often diagnosed 5-7 years after symptom onset, leading to severe complications like blindness, kidney failure, heart attacks, stroke, and limb amputation. About 50% of T2D patients develop diabetic neuropathy, which can damage nerves throughout the body, including those controlling the vocal cords, leading to issues like vocal fold paralysis, hoarseness, or vocal strain. This partnership aims to develop novel machine learning solutions that detect Type 2 diabetes and prediabetes by analysing voice patterns, providing a non-invasive and innovative method for early diagnosis. Specifically, this project will develop novel deep learning algorithms, and audio and vision transformers with various attention mechanisms, to identify early warnings and prediabetes/diabetes conditions. 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/MSc 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 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. 

The post offers a highly competitive rewards and benefits package including:

  • £2,000 per year Personal Development budget (exclusive of salary) and 10% of project time allocated to personal development
  • Coaching and mentoring by an Innovate UK KTP Advisor
  • Access to University facilities for work development
  • Opportunities of engaging with a wide range of stakeholders

Besides that, the post also offers the following additional benefits:

  • Generous annual leave entitlement 
  • Training and Development opportunities
  • 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 at DDM Health Ltd., Technology House, Science Park, University of Warwick, Coventry, CV4 7EZ.   The Associate is also expected to attend project meetings and training events in Egham, Surrey where the University is situated in a beautiful, leafy campus near to Windsor Great Park and within commuting distance from London. 

Since this is a KTP project funded by Innovate UK and DDM Health, it is essential you understand how KTP works and the vital role you will play if you secure this position. To learn more please visit: https://iuk-ktp.org.uk

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: 0425-090

Closing Date:   23:59, 5 June 2025 

Interview Date: 13 June 2025

Further details:    Job Description & Person Specification    
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The university has adopted hybrid working for some roles therefore some remote working may be possible for this role.

Royal Holloway is committed to equality, diversity and inclusion (EDI), and encourages applications from all people regardless of age, disability, gender, marital status, parental status, race, religion or belief, sexual orientation, or trans status or history. More information on our structures and initiatives around EDI, including information on staff diversity networks, can be found on our Equality and Diversity Intranet page.


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