• National University of Singapore Featured PhD Programmes
  • Northumbria University Featured PhD Programmes
  • University of Surrey Featured PhD Programmes
  • University of East Anglia Featured PhD Programmes
  • University of Glasgow Featured PhD Programmes
  • University of Leeds Featured PhD Programmes
  • University College London Featured PhD Programmes

Postgrad LIVE! Study Fair

Edinburgh

University of Birmingham Featured PhD Programmes
National Institute of Agricultural Botany (NIAB) Featured PhD Programmes
Loughborough University London Featured PhD Programmes
Newcastle University Featured PhD Programmes
University of Southampton Featured PhD Programmes

Mobile Technologies in Healthcare

This project is no longer listed in the FindAPhD
database and may not be available.

Click here to search the FindAPhD database
for PhD studentship opportunities
  • Full or part time
    Dr M Villa-Uriol
  • Application Deadline
    No more applications being accepted
  • Funded PhD Project (Students Worldwide)
    Funded PhD Project (Students Worldwide)

Project Description

The Department of Computer Science at the University of Sheffield is offering a fully funded 3-year PhD studentship on Mobile Technologies in Healthcare.

About the Department
The Department of Computer Science was established in 1982 and has since attained an international reputation for its research and teaching. In the recent Research Excellence Framework (REF2014), 45% of the research in the department was recognised as internationally excellent in terms of originality, significance and rigour, and another 47% as internationally world leading. These results place the department among the top 5 UK Computer Science departments for research excellence.

Background
Our society is continuously experiencing the pervasive presence of mobile devices with sophisticated sensing capabilities. This, combined with the Internet of Things, has made possible the capture and sharing of valuable data about individuals and their environment. This has a tremendous potential in healthcare not only at the patient’s level but also from a public health perspective.

In the clinical domain, the increase in health screening programmes and incidental findings of diseases have translated in a wide availability of heterogeneous data (images, physiological signals and medical test results). The combination of these allow the creation of sophisticated and highly detailed physiologically-based personalised models of diseases from which to derive new biomarkers with a large potential diagnostic and prognostic power.

Research Topic
This research project aims to combine the power of personalized modelling of diseases, mobile technologies and big data analytics to aid clinicians and patients to use effectively the wealth of data currently available. Incomplete, inaccurate and fragmented data need to be taken into account to quantify the uncertainty in the clinical recommendations. This will require to research on novel methodologies and solutions able to make sense of the wealth of data currently available. The primary clinical focus will be on serious long-term conditions affecting the cardiovascular system (e.g. hypertension, coronary heart disease, stroke), respiratory system (e.g. asthma, chronic obstructive pulmonary disease), or neuromusculoskeletal system (e.g. motor neuron disease, Parkinson’s disease). The successful applicant will be working in the field of Mobile Technologies, Clinical Decision Support, Health Informatics and Medical Knowledge Management to offer innovative, integrative and original solutions with great impact. A special emphasis will be put on the clinical validation and adoption of the researched solutions.

About INSIGNEO Institute and OAK Research Group
This studentship offers a unique opportunity to work in the context of the Virtual Physiological Human initiative, and will be supported by the expertise available within the INSIGNEO Institute for in silico Medicine, and the Organisations, Information and Knowledge (OAK) research group. The INSIGNEO Institute was launched publicly in May 2013 between the University of Sheffield (Faculties of Engineering and Medicine) and the Sheffield Teaching Hospitals NHS Foundation Trust. This position will enable the applicant to access INSIGNEO’s rich network of clinical contacts. Oak has a proved track record in big data, knowledge management and the use of mobile technologies with the purpose of monitoring and making sense of the environment.

The Candidate
Applicants should have, or expect to achieve, a minimum of an upper-second-class Honours degree (2.1 or above) or a Master´s degree (or equivalent) in Computer Science, Mathematics/Statistics, or related disciplines. Demonstrable knowledge of mobile/web programming, machine learning, and big data are desirable. Previous knowledge of the clinical subject area is not required, although the candidate should demonstrate an interest to learn those clinical aspects that are relevant to the project. Good analytical thinking, strong programming and interpersonal skills are essential.

How to Apply
Applicants need to apply before July 20, 2016 using the online application form at:
http://www.sheffield.ac.uk/postgraduate/research/apply
For more information on the PhD Programme at The University of Sheffield:
http://www.sheffield.ac.uk/postgraduate/research/sheffield

Please send informal enquiries to Dr. Maria-Cruz Villa-Uriol, [Email Address Removed]
Dr. Maria-Cruz Villa-Uriol’s webpage: http://staffwww.dcs.shef.ac.uk/people/M.Villa-Uriol/

Funding Notes

The award covers UK/EU tuition fees and a stipend at the standard UK research rate of £14,296 per annum. Funding is available for conference attendance and research visits to partner organisations.

UK applicants and EU applicants are eligible for a full scholarship award. International students are eligible to apply, however will have to pay the difference between home rate and international fees.

Related Subjects

How good is research at University of Sheffield in Computer Science and Informatics?

FTE Category A staff submitted: 30.50

Research output data provided by the Research Excellence Framework (REF)

Click here to see the results for all UK universities

Cookie Policy    X