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.

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.

This studentship offers a unique opportunity to work in the context of the Virtual Physiological Humaninitiative, and will be supported by the expertise available within the INSIGNEO Institute for in silicoMedicine, 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.

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.

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.

Please send informal enquiries to Dr. Maria-Cruz Villa-Uriol, [email protected]

Dr. Maria-Cruz Villa-Uriol’s webpage: http://staffwww.dcs.shef.ac.uk/people/M.Villa-Uriol/