Opportunities and Vacancies


Bio-Health Informatics Education & Development Associate

Bio-Health Informatics Education & Development Associate

The University of Manchester delivers a world-leading portfolio of eHealth research and training across the north of England and beyond. An opportunity has arisen for a Bio-Health Informatics Education and Development Associate to join the Greater Manchester Connected Health Cities (GM CHC) project to support the design and delivery of a range of teaching materials, engage in relevant scholarship, professional and knowledge exchange activities with support and guidance from Head of Education and other senior colleagues; and to carry out training administrative tasks assigned by the GM CHC Senior Management Team.

This is a varied and dynamic role in a fast paced environment and you will need to be comfortable producing a range of teaching materials, gather and assess learning requirements of multiple stakeholders and engage in collaborative scholarship activities in a manner that supports a research-informed approach to teaching. We are looking for someone who is ideas-driven, enthusiastic and able to work well as part of the wider GM CHC team.

You will be educated to degree level or equivalent in an area relevant to health informatics; have significant experience of delivering education or training in health and social care, and have demonstrable experience of developing and delivering a variety of educational models and original materials for a range of formats including face-to-face, eLearning and blended learning.

This is a fantastic opportunity to use your skills and knowledge to drive innovation in the health and social care sector and make a lasting impact.

Informatics, Imaging & Data Sciences, School of Health Sciences, Oxford Road, Manchester.

Deadline for applications: 23rd February 2017.

Source: Jobs at The University of Manchester


Other Opportunities

PhD Studentship: The development of health risk assessment models including spatio-temporal components with applications in the risks of health care associated infections (HAIs)

PhD Studentship: The development of health risk assessment models including spatio-temporal components with applications in the risks of health care associated infections (HAIs)

Risk prediction models are increasingly used within health care settings to manage patients and resources. The Scottish Healthcare Associated Infection Prevention Institute (SHAIPI) has a 3 year work stream to develop risk prediction models for health care associated infections (HAIs) in Scotland using routinely collected national, individual level data from hospitals, general practice and pharmacies.

Traditional risk prediction models use historical data collected on individuals to predict a 1 year, 5 year, or lifetime risk of a disease. Risk prediction for HAIs, however, is challenging due to temporal and fluctuating changes in risk due events such as admissions to hospital, which after a time the risk may return to a baseline level. In our research group we have recently published work the risk factors for a specific HAI, Clostridium, work which forms the cornerstone of the SHAIPI programme, whereby we will assess the ability of our model to predict HAI occurrence given previous antibiotic prescribing and other individual level risk factors. There is also potentially a spatial aspect to therisk of community acquisition of some HAIs for an individual, or groups of individuals. Aquisition may depend not only on the characteristics of the individual, for example the prescribing history, but also on the characteristics of the area (GP practice) that the individual attends. GP Practices within the same local area will share characteristics, some as a function of being within the same community health partnership and health board.

The successful PhD student will:

(a) To write a comprehensive review of existing literature on statistical features of risk prediction models including the types of data sets that are used for the development of these models. This will classify the approaches used and identify the methodological gaps.
(b) develop spatio-temporal risk prediction models, test the model selection process and use simulated data with various types of spatial and temporal effects to validate the model predictions.
(c) derive a model for community acquired Clostridium Difficile and to assess the contribution of the spatial and temporal components of risk.
(d) develop novel data visualisation tools to display the risk predictions against comparator groups and to enable the user to investigate how the risk is modified by changes to the characteristics of the individual and the GP Practice.

For full details see: www.findaphd.com/search/ProjectDetails.aspx?PJID=83447&LID=1469

Deadline for applications is 10th March 2017 with interviews being held on 31st March 2017.


Back to top+

Keep in touch

Contact us or join our mailing list to stay up to date with the latest news and events.