Machine Learning in 3D Wound Care
Dysgu Peiriant Mewn Gofal Clwyfau 3D
Project ID: 21099
Application Deadline: 31st May 2020
|MbyRes wedi’i gyllido yw hwn, sy’n cynnwys cyflog hael a ffioedd dysgu, gydag amgylchiadau sydd â digon o adnoddau ar gyfer ysgoloriaeth lwyddiannus.||This is a funded MastersbyResearch, including a generous stipend and tuition fees, with well-resourced circumstances for a successful scholarship.|
Here is an exciting opportunity to apply computerised machine-learning technology to improve the automated clinical assessment of chronic skin wounds and ulcers. This could lead to entirely new approaches in wound diagnosis, management and wound care treatment.
This is a funded MRes, including a generous stipend and tuition fees, with well-resourced circumstances for a successful scholarship.
The selected candidate will apply their research and interpersonal skills and ambition to conduct applied research with the University of South Wales and GPC Ltd. This research project will allow the student to develop transferable knowledge and skills in this most exciting and active field of applied health and wound care research.
This Knowledge Economy Skills Scholarship (KESS) project will be held in the Faculty Life Sciences at the University of South Wales. KESS is a programme funded by the European Social Fund (ESF) awarded by the Welsh European Funding Office (WEFO) in the Welsh Government. The MRes will be associated with University Health Board. The project will collect images of wounds including depth data from different groups of patients using 3D cameras. GPC Ltd have experience in this area and are supporting this project to assist in the development of techniques that can better assist the care, interventions and hence outcomes of these patients. In particular, there are a number of areas where the application of various machine learning techniques and tools will be applied to assist the clinician.
Company partner: GPC Ltd
GPC is a software development company with a number of healthcare products. They are particularly interested in emerging technology such as 3D, Machine Learning and Augmented Reality (AR). As such, their products are particularly innovative. Whilst they have some excellent in-house knowledge and experience they are looking to further add to their products with research and development in the areas mentioned for wound care.
Programme of research:
The project will run for 12 months. Under supervision and guidance, the project will use various machine learning techniques and tools to automate identification of wound types and grade pressure ulcers. Using processes such as 3D image enhancement and fully automated edge tracing. If time allows predictive analytics will be used to derive and consider patient outcome reports.
The participant will be based at the University of South Wales although there will be scope to spend appropriate time at GPC Ltd. Company training and induction will be provided as it is for all staff.
The participant will be provided with a laptop and any software licenses required such as Microsoft Visual Studio and MSDN. GPC will provide technical support and guidance, in particular with the design of algorithms and the integration with GPC’s software to ensure the outputs are suitable from a technical and commercial stance.
GPC will provide access to customers and medical guidance and access to existing development platforms including but not limited to development servers, cloud test environments, broadband, telephones, and office space.
Bydd yr ysgoloriaeth ymchwil yn cynnwys ffioedd ar gyfer rhaglen MbyRes llawn-amser ac yn talu cyflog o oddeutu £11k y flwyddyn. Mae hefyd tua £3k o gostau cymorth ar gael i’r prosiect ar gyfer treuliau, teithio, mân offer, hyfforddiant (gan gynnwys Ysgol Raddedig KESS) a mynychu cynadleddau.
Mae’r swydd ar gael o 1 Hydref 2020
The studentship will cover the fees for a full-time MbyRes programme and pay a stipend of circa £11k p.a. There is also around £3k project support costs available for consumables, travel, minor equipment, training (including the KESS Grad School) and conference attendance.
The position is available from 1st October 2020
I fod yn gymwys i gael ysgoloriaeth ymchwil KESS, mae’n rhaid:
· cael cyfeiriad cartref yn yr ardal Gydgyfeirio (manylion isod)* ar adeg cofrestru.
· bod gennych hawl i ymgymryd â gwaith cyflogedig yn yr ardal Gydgyfeirio* ar ôl cwblhau’r ysgoloriaeth.
· bod y Brifysgol yn eich ystyried yn fyfyriwr ‘cartref’ neu ‘UE’ at ddibenion ffioedd dysgu yn unol â chanllawiau’r Brifysgol.
· eich bod yn bodloni meini prawf mynediad Prifysgol De Cymru: gweler isod, cymwysterau a phrofiad a’r broses ymgeisio
*Mae’r ardal Gydgyfeirio’n cynnwys Gorllewin Cymru a’r Cymoedd, ac yn cynnwys y 15 awdurdod lleol canlynol: Ynys Môn, Gwynedd, Conwy, Sir Ddinbych, Ceredigion, Sir Benfro, Sir Gaerfyrddin, Abertawe, Castell-nedd Port Talbot, Pen-y-bont ar Ogwr, Rhondda Cynon Taf, Merthyr Tudful, Caerffili, Blaenau Gwent a Thorfaen.
Eligibility of Student:
To be eligible to hold a KESS studentship, you must:
· have a home address in the Convergence area (details below)* at the time of registration.
· have the right to take up paid work in the Convergence area* on completion of the scholarship.
· be classified by the University as ‘home’ or ‘EU’ for tuition fees purposes according to the University’s guidelines.
· satisfy University of South Wales’s admissions criteria: see below, qualifications and experience and application process
*The Convergence area covers West Wales and the Valleys, and is made up of the following 15 local authorities: Isle of Anglesey, Gwynedd, Conwy, Denbighshire, Ceredigion, Pembrokeshire, Carmarthenshire, Swansea, Neath Port Talbot, Bridgend, Rhondda Cynon Taf, Merthyr Tydfil, Caerphilly, Blaenau Gwent and Torfaen.
|Cymwysterau a phrofiad:
Bydd gan ymgeiswyr cymwys:
|Qualifications and experience:
Eligible applicants will:
- Have a degree (2i or higher) in an appropriate computing, engineering, medical physics discipline. Nursing and Life Science graduates may also apply.
- Possess a reasonable understanding of machine learning, image processing and predictive technologies. Some understanding of human physiology, and wound pathophysiology would be useful but not essential.
- Be highly self-motivated, with capacity to learn and develop applied research techniques
- Have well-developed and positively collaborative interpersonal skills
- Have an ability to deliver technical reports and communicate findings
- Be willing to travel and work in academic/ industrial / clinical / community support settings
|Y Broses Ymgeisio:
I lawrlwytho’r pecyn ymgeisio, ewch i: Pecyn Ymgeisio Cyfranogwr
Am unrhyw ymholiadau ynghylch cymhwysedd, cysylltwch â: Tîm KESS, Gwasanaethau Ymchwil ac Arloesedd, Prifysgol De Cymru: firstname.lastname@example.org Ffôn: 01443 482578
Ar gyfer ymholiadau anffurfiol neu ragor o wybodaeth am y rhaglen, cysylltwch â:
Prof Joyce Kenkre – email@example.com
To download an application package, please visit: Participant Application Package
For any queries on eligibility, please contact: KESS Team at Research and Innovation Services, University of South Wales: firstname.lastname@example.org Tel: 01443 482578
For informal enquiries or further programme information, please contact:
Prof Joyce Kenkre – email@example.com
|Dyddiad cau ar gyfer ceisiadau: Hanner nos Dydd Sul 31 Mai 2020
Cynhelir cyfweliadau yn ystod yr wythnos yn dechrau (i’w gadarnhau)
|Closing date for applications: Midnight Sunday 31st May 2020
Interviews will be held w/c (TBC)
|Mae Ysgoloriaethau Sgiliau Economi Gwybodaeth (KESS) yn fenter sgiliau lefel uwch ar draws Cymru a arweinir gan Brifysgol Bangor ar ran y sector AU yng Nghymru. Fe’i cyllidir yn rhannol gan raglen gydgyfeirio Cronfa Gymdeithasol Ewropeaidd (ESF) Llywodraeth Cymru ar gyfer Gorllewin Cymru a’r Cymoedd.||Knowledge Economy Skills Scholarships (KESS) is a pan-Wales higher level skills initiative led by Bangor University on behalf of the HE sector in Wales. It is part funded by the Welsh Government’s European Social Fund (ESF) convergence programme for West Wales and the Valleys.