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Knowledge Transfer Partnership (KTP) Associate
| Dyddiad hysbysebu: | 25 Ebrill 2025 |
|---|---|
| Cyflog: | £40,839 i £48,003 bob blwyddyn |
| Gwybodaeth ychwanegol am y cyflog: | Including London Allowance |
| Oriau: | Llawn Amser |
| Dyddiad cau: | 25 Mai 2025 |
| Lleoliad: | Coventry, West Midlands |
| Gweithio o bell: | Hybrid - gweithio o bell hyd at 2 ddiwrnod yr wythnos |
| Cwmni: | Royal Holloway University of London |
| Math o swydd: | Cytundeb |
| Cyfeirnod swydd: | 0425-090 |
Crynodeb
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 in DDM Health Ltd. (Coventry). 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
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 in DDM Health Ltd. (Coventry). 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
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