EU/UK PhD Scholarship in Healthcare Machine Learning and Text Mining, Swansea University, UK
Closing date: 31 July 2016
Subject study: Combined use of natural language processing (NLP), text mining, machine learning and predictive data analytics to establish data-driven insights into diseases from SNOMED CT derived clinical narratives and electronic health records.
Scholarships are collaborative awards with external partners including SMEs and micro companies, as well as public and third sector organisations. The scholarship provides 3 years of funding with a 6 month period to complete the thesis. The achievement of a postgraduate skills development award (PSDA) is compulsory for each KESS 2 scholar and is based on a 60 credit award.
Eligibility
This PhD Scholarship is offered for UK or EU applicants.
Funding
The PhD studentship covers the full cost of UK/EU tuition fees, plus a stipend. The bursary will be limited to a maximum of £14,002 p.a. dependent upon the applicant's financial circumstances as assessed in section C point 4 in the KESS 2 participant proposal form (http://www.swansea.ac.uk/media/Guidance - Participant Proposal Swansea version 2.docx).
There will also be additional funds available for research expenses.
You will be supervised by Professor Ronan Lyons, Dr Shang-Ming Zhou and Mr Phil Davies.
Applicants are strongly advised to contact Shang-Ming Zhou ([log in to unmask]) or Rhydian Owen ([log in to unmask]) regarding information on the area of research.
How to Apply
To apply:
(http://www.swansea.ac.uk/media/KESS II Draft Student application form Swansea 2015-16.docx)
(http://www.swansea.ac.uk/media/KESS Sup Form 2016.doc)
Please return both application forms and supporting documentation to the KESS office at the address stated on the KESS application form or email Jane Kelly ([log in to unmask]/01792 513565) or Jane Phipps ([log in to unmask]/01792 513729).
For all other queries please contact the KESS office contacts mentioned above.
The deadline for applications is Tuesday 31st July 2016.
For more information, please visit: