Opportunities and Vacancies

Vacancies

Teaching Fellow

Teaching Fellow

Hours per week: full time
Location: Manchester
Salary: £31,604 – £38,833 per annum

Closing date: 4th June 2018

We are seeking a motivated, self-starting individual is to fulfil the role of Teaching Fellow at our varied and stimulating Centre for Health Informatics at the University of Manchester.

The post holder will manage, administer and teach a range of educational activities at postgraduate and CPD levels in health informatics and health data science; be the key interface for prospective and enrolled participants/students; support teaching curricula, skills audits and external recruitment and engagement.

Informal enquiries may be made to Georgina Moulton: georgina.moulton@manchester.ac.uk

https://www.jobs.manchester.ac.uk/internal/displayjob.aspx?jobid=15346

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

National PhD Training Programme in Antimicrobial Resistance (AMR) research

National PhD Training Programme in Antimicrobial Resistance (AMR) research

Applications are now open for the first and only National PhD Training Programme in Antimicrobial Resistance (AMR) research, funded by the Medical Research Foundation in response to the urgent action needed to halt antimicrobial resistance and to accelerate new treatments for bacterial infection.

There are 18 fully-funded PhD studentships in total, hosted by 15 universities & institutions across the UK.
Applications are invited for one PhD Studentship which will be selected from one of the three projects below. The projects fall under the theme of Behaviour within and beyond the healthcare setting. The project offered under this theme aims to identify the socio- economic conditions and behavioural attitudes that drive the spread of resistant bacteria to help develop and evaluate policies and strategies to mitigate and manage AMR and the stewardship of antibiotics.

Project one 
Optimising the Design and Delivery of Audit and Feedback to Improve Antibiotic Prescribing Behaviour in Hospital
Project two
Recent Migration and Risk of Antimicrobial Resistant Infection: a Cohort Study Using Linked Electronic Health Records
Project three
Storytelling to Communicate the Concept of AMR to the Public

https://www.ucl.ac.uk/prospective-students/graduate/research/degrees/health-informatics-multiprofessional-education-mphil-phd
NPIF Studentships in Data Science or Artificial Intelligence

NPIF Studentships in Data Science or Artificial Intelligence

UCL-Birkbeck MRC DTP

Three fully funded 3.5 year PhD studentships in AI or Data Science to start in September 2018.

UCL, with its top ranked (REF2014) Computer Science department – including a new Centre for AI in Computer Science – three NIHR Biomedical Research Centres (sharing £167 million in funding), and its strong connections with Health Data Research UK, the Alan Turing Institute, industry and NHS partners is uniquely positioned to deliver doctoral training in AI and Data Science.  Students will have access to unique collections of patient data, covering 15 million different patients and including images, genomic data, electronic health records and linked data from primary and secondary care. They will work in real-world clinical settings (with a partner hospital NIHR Biomedical Research Centres) to ensure they have a practical understanding of the value and limitations of their datasets and acquire the translational skills required to put systems into practice.

Students will be able to access a distinctive interdisciplinary training programme, based in the Institute of Health Informatics, covering Health Data Science, Health Informatics and Health Data Analytics. Students will attend a unique annual programme of short courses in electronic health records research and access training in AI programming languages and data science techniques offered across UCL faculties. Students will also be eligible for a unique range of internships, including those offered through UCL’s Public Policy Placement Scheme, and others with partners including NHS Digital, Public Health England and companies such as IQVIA, Astra Zeneca and Roche.

Deadline for applications: Midday Monday 2nd July.

For further information and details on how to apply visit the UCL-Birkbeck MRC DTP website.

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: Defining the relationship between rheumatoid arthritis, comorbidity, and adverse health-related outcomes: A precision medicine approach

PhD studentship: Defining the relationship between rheumatoid arthritis, comorbidity, and adverse health-related outcomes: A precision medicine approach

College of Medicine, Veterinary and Life Sciences, University of Glasgow
Location: Glasgow
Salary: Stipend of at least at least £14,553

Starting date: September 2018

MRC DTP in Precision Medicine

Up to 35 fully funded studentship positions are available across the University of Glasgow and Edinburgh. Our next intake will be for PhD projects commencing September 2018.

The Precision Medicine Doctoral Training Programme (DTP) offers PhD with Integrated Study studentships funded by the Medical Research Council (MRC), The University of Edinburgh and the University of Glasgow. Hosted by the University of Edinburgh in collaboration with the University of Glasgow and the Karolinska Institute, this prestigious programme provides PhD research training alongside taught courses over four years of study and welcomed its first cohort of students in September 2016.

This Doctoral Training Programme focuses on training PhD students in key MRC skills priorities in quantitative skills (mathematics, statistics, computation, and developing digital excellence) as applied to variety of data sources (from ‘omics’ to health records), and interdisciplinary skills including imaging and stratified medicine.

https://www.findaphd.com/search/projectdetails.aspx?PJID=91959
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: 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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