Health Informatics: KESS II Funded PhD Studentship: Healthcare Data Analytics and Text Mining

Closing date: Apply as soon as possible

Key Information

*This scholarship is part funded by the Welsh Government’s European Social Fund (ESF) convergence programme for West Wales and the Valleys.*

Subject study:

Health informatics, natural language processing, machine learning, text analytics, public health, epidemiology

Start date: January 2018

Key Information:

Healthcare systems have collected mountains of textual and numeric patient records about disease activities, hospital admissions and visits, drug prescriptions, physician notes and more. But medical research and related industries like pharmaceutical industry are faced with enormous challenges as a result of the very restrictive handling of such health data.

This PhD studentship offers an exciting opportunity of exploring and /or developing machine learning, natural language processing and text analytics techniques to extract valuable knowledge from SNOMED CT derived clinical narratives. Such knowledge will enable better care, prognosis of patients, promotion of clinical and research initiatives, fewer medical errors and lower costs, and thus a better patient life.  

This project will involve industrial collaboration with Clinithink Ltd.

The successful student will have the chance of working in a very dynamic academic research environment offered by the world class UK Farr Institute of Health Informatics Research (http://www.farrinstitute.org/). We make up one part of this Institute – CIPHER (The Centre for Improvement in Population Health through E-records Research):http://www.swansea.ac.uk/medicine/research/researchthemes/patientpopulationhealthandinformatics/ehealth-and-informatics-research/

You will be supervised by Professor Ronan Lyons, Dr Shang-Ming Zhou and Mr Phil Davies. 

Scholarships are collaborative awards with external partners including SME’s 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 II scholar and is based on a 60 credit award.

Eligibility

Applicants should have a minimum of a 2.1 undergraduate degree and/or a Master's degree (or equivalent qualification) in Computer Science, Computational Linguistics, Computing, Data science, Statistics, Epidemiology, Health informatics, Medical Informatics, Bioinformatics, or any other related areas.

This PhD scholarship is open to UK or EU applicants, or applicants with indefinite leave to remain in the UK.

For more information on eligibility criteria please refer to section C of the  KESS II Participant Proposal Form

Any queries relating to Section C – Eligibility, please contact KESSstudentenquiries@swansea.ac.uk.

For details on the University’s English Language entry requirements, please visit – http://www.swansea.ac.uk/admissions/englishlanguagerequirements/

 

Funding

The studentship covers the full cost of UK/EU tuition fees, plus a stipend.  The bursary will be limited to a maximum of £14,198 p.a. dependent upon the applicant's financial circumstances as assessed in section C point 4 on the  KESS II Participant Proposal Form

There will also be additional funds available for research expenses.

How to Apply

Applicants are strongly advised to contact Dr Shang-Ming Zhou regarding information on the area of research, by email or by telephone: (s.zhou@swansea.ac.uk / +44 (0)1792 602580).

To apply:

  1. Complete the  KESS II Participant Proposal Form
  2. Complete the  KESS Supplementary Application Form
  3. You will also need to provide copies of the following documentation:
  • Degree certificates
  • Academic transcripts
  • References
  • CV
  • English language certificate (if required)
  • All supporting documentation as detailed in Section C of the KESS II Participant Proposal Form

Please return both application forms and supporting documentation to the KESS office at the address stated on the KESS application form (original ink signatures only).

For any other queries, please contact: KESSstudentenquiries@swansea.ac.uk

Applicants should apply as soon as possible.