Opportunities and Vacancies

Vacancies

Senior Lecturer in Quantitative Methods for Admin Data Research

Senior Lecturer in Quantitative Methods for Admin Data Research

ADRC-E are currently advertising for a Senior Lecturer in Quantitative Methods for Admin Data Research.

The post holder will aim to advance and widen the use of quantitative methods for research using linked administrative data, to build capacity in this area through attracting grants and will contribute to teaching, and to developing the scientific strategy within the Population, Policy & Practice UCL Great Ormond Street Institute of Child Health (PPP UCLGOSICH). The post holder will also be expected to develop and lead an area of research which could focus on methodology or be a programme of research applied to policy or organisation of services. Relevance to children or families is desirable. The research should address the aims of the ADRC-E, which are to widen the use of administrative data for research.

The post would be suitable for a highly skilled quantitative Senior Lecturer who wants to address the challenges of using linkage of large administrative datasets to inform policy, service development, and/or methodological advances.

Closing date: 30 September 2017
Interview date: 13 October 2017

For key requirements, full job description, and how to apply, please visit https://goo.gl/LuP5Sz.

https://goo.gl/LuP5Sz

Other Opportunities

Fully funded PhD at the Department of Biostatistics, University of Liverpool

Fully funded PhD at the Department of Biostatistics, University of Liverpool

Web usage data in clinical trials – how can we determine dose?

The use of web based interventions in clinical trials is on the increase, as the internet provides an accessible mechanism for delivering intervention without the need for participants to travel to a clinic. However, it does not appear that the effectiveness of web interventions is being linked back to the actual usage of the intervention.

Collection of intervention use, or “dose”, data is important to inform causal analysis methods accounting for actual intervention use (rather than simply analysing according to randomisation, using “intention to treat”). In the context of an online intervention, however, it is not immediately obvious how best to define and measure “dose”.

Participants’ use of an online intervention can be recorded and monitored using various techniques, including Google Analytics (GA), server log data and customisable call backs to the projects servers; however, the reliability of these approaches is not guaranteed. In particular, it is likely that many users are unaware of the inaccuracies associated with GA data.

This project seeks to guide trialists on best practice of collection and use of online intervention usage data, to ensure consistent and reliable comparisons of web intervention “dose” to be evaluated across studies. In-house generated web usage data and trial data will be used to compare GA data with in-house server log and video data, to demonstrate the extent of GA inaccuracies and determine best practice for capturing accurate representation of web usage. The project will demonstrate how to use web usage data to inform causal analyses, to ascertain whether certain patterns of web use correspond to improved outcome, and will culminate in guidelines and toolkit for trialists on how best to collect, report and analyse web usage data as part of online intervention trials.

Person specification: The ideal candidate would demonstrate an interest in the use of web based interventions for clinical research, with a BSc/MSc in a relevant discipline demonstrating statistical/numerical skill.

Training and support: The student would be provided with all relevant IT and statistical training by supervisors in Liverpool and colleagues in Lancaster, Nottingham and UCL.

For further information contact Dr Susanna Dodd (s.r.dodd@liv.ac.uk) or visit www.findaPhD.com

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

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

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.

The successful student will have the chance of working in a very dynamic academic research environment offered by the world class The Farr Institute of Health Informatics Research. We make up one part of this Institute – CIPHER (The Centre for Improvement in Population Health through E-records  Research).

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

This scholarship is part funded by the Welsh Government’s European Social Fund (ESF) convergence programme for West Wales and the Valleys. The project will involve industrial collaboration with Clinithink Ltd.

The successful candidate is expected to start their PhD scholarship in July or October 2017.

Further details and How to apply

www.swansea.ac.uk/postgraduate/scholarships/research/health-informatics-kess-phd-healthcare-data-analytics.php#accept
PhD Studentship: Deep Learning Discovery & Visualization from Longitudinal Data

PhD Studentship: Deep Learning Discovery & Visualization from Longitudinal Data

This project aims to apply deep learning techniques on the large, rich bioresources available in Dundee (e.g. goDARTS) to discover novel biomarkers for high-prevalence conditions like diabetes and complications, CVD and dementia.

The focus is on deep learning, a class of machine learning algorithms which has pushed forward the field dramatically, and is increasingly being deployed in data mining/data analytics in genomics, healthcare and many other disciplines.

The student will learn about deep learning and deep network architectures as well as statistical packages (e.g. R) and “conventional” statistical methods.

The aim is to explore the potential of deep learning to discover biomarkers in image data (especially), and to visualize the relevant image features in a way suitable for human interpretation.

The project is a collaboration with the VAMPIRE/CVIP group in Computing (SSEN), which hosts state-of-the-art NVIDIA hardware to run specialised DL architectures.

(This PhD Studentship is funded by the MRC)

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PhD Studentship: Development of Innovative Methods for the Provision of Routinely Collected Medical Images Within Secure Safe-haven Environments

PhD Studentship: Development of Innovative Methods for the Provision of Routinely Collected Medical Images Within Secure Safe-haven Environments

Background

The medical digital images are routinely collected and archived by the NHS. Such images, especially when linked to other routinely collected health data, are extremely useful for research into areas including: early/preclinical diagnosis, disease progression, validation of treatment methods, development of novel computer vision methods for biomarker extraction, validation of novel algorithms and machine learning approaches and discovery and classification of disease types. Research using routinely collected imaging data for research has historically been under-utilised for many reasons including the impracticality of seeking consent of patients, challenges of de-identification of image data, absence of non-proprietary software for efficient handling this type of “big data” for research.

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PhD Studentship: Genomics & Retinal Biomarkers of Neurodegeneration

PhD Studentship: Genomics & Retinal Biomarkers of Neurodegeneration

This project is part of our investigation into novel biomarkers for risk assessment of neurodegenerative conditions, mainly dementia, from a rich set of patient data including full genetic profiles. The student will apply statistics and data analytics techniques (machine learning, deep learning), and learn to use state-of-the-art image measuring packages (e.g. VAMPIRE for retinal images) . It leverages large, cross-linked bioresources (eg goDARTS) which our team have accessed in previous projects. This project builds upon a 1.1M EPSRC grant (with Edinburgh) on multi-modal retinal biomarkers for vascular dementia (2015-8) and a Leverhulme project (2012-5) on retinal measurements and genetics, and complements starting projects like the 5M NIHR Global Health programme grant on diabetes-related data analytics. It is a strong strategic fit with our University strategy on health and biomedical informatics.

(This PhD Studentship is funded by the MRC)

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PhD Studentship: Integrating genomic and electronic health data for drug-discovery

PhD Studentship: Integrating genomic and electronic health data for drug-discovery

Funding: £16,533 per annum (inside London). A fixed sum of £8,000 pa for four years (exclusive of VAT) will be paid by GSK to support the Studentship. This will be used to supplement the annual maintenance (up to £4,000 pa) and support research expenses. In addition £350 pa of GSK’s funds will be allocated for the Student’s travel to scientific meetings.
Closing date: Wednesday 30thth August 2017
Location: University College London

This is a 4-years full-time PhD studentship funded by Engineering and Physical Sciences Research Council (EPSRC) industrial CASE studentship, supported through the National Productivity Investment Fund (NPIF).

The drug discovery process faces major challenges that threaten its sustainability, and this has wider implications to society in general.

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PhD Studentship: Personalising Primary Care Prescribing: Developing & Evaluating Integrated Informatics to Embed Pharmacogenomics Information into Clinical Decision-making in Real-time

PhD Studentship: Personalising Primary Care Prescribing: Developing & Evaluating Integrated Informatics to Embed Pharmacogenomics Information into Clinical Decision-making in Real-time

Background

There has been a revolution in our understanding of the genomics of drug metabolism, but apart from the treatment of cancer, this understanding has not much influenced clinical practice despite tens of thousands of Tayside residents having been genotyped and despite almost all primary care prescribing being done electronically. Key challenges include: (1) Prescribing decisions are made very quickly in a time-pressured environment and both under- and over-alerting are known to pose safety risks; (2) Linking clinical data in real-time poses information governance and security problems. The aim of this project is to develop and evaluate an informatics tool to make useful pharmacogenomics information available at the point of primary care prescribing.

(This PhD Studentship is funded by the MRC)

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PhD Studentships: Population Health and Epidemiology

PhD Studentships: Population Health and Epidemiology

Funding: Annual stipend of £14,553 per year for 3 years plus UK/EU University tuition fees
Closing date: Friday 18th August 2017
Location: University of Edinburgh

Two fully funded PhD Studentships are available as part of The Farr Institute Scotland investment in health informatics research. The successful applicants will be working at the forefront of health informatics and administrative data research in the UK and will be conducting research in and around the general area of environments and health.

One studentship will focus on health inequalities and by exploiting the full population nature of administrative data sources will examine how these patterns vary across urban, rural and island communities in Scotland. The aim will be to better understand how economic difference acts within different social contexts to produce health inequalities.

The second studentship will focus on health within households. Recent medical advances have allowed chronically ill children and youths to live longer and to reside at home in the care of their families rather than in a medical institution. Children, their parents and their siblings now have to adapt and cope with chronic illness as a stressor in family life and this project will examine how these stressors affect the health of other members of the family.

Both projects will make use of administrative health and social data and will work closely with colleagues in the Administrative Data Research Centre and the Farr Institute as well as research groups in the School of Geosciences including the centre for research on environment society and health (CRESH) and the population, health and place research group.

Find out more and apply.

(This PhD Studentship is funded by the MRC)

https://www.findaphd.com/search/ProjectDetails.aspx?PJID=87842
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