Dear all
I regret that there is a problem with the University of Manchester's
job application site and this cannot currently be accessed. I
apologise for the inconvenience. Meanwhile please do get in touch with
me for informal enquiries regarding the below position.
Best wishes
Matt
On Thu, Jul 21, 2016 at 12:26 PM, Matthew Sperrin <[log in to unmask]> wrote:
> Closing date : 21/08/2016
> Reference : BM&H-08580
> Faculty / Organisational unit : Biology, Medicine & Health
> School / Directorate : School of Health Sciences
> Division : Division of Population Health, Health Services Research and
> Primary Care
> Employment type : Fixed Term
> Duration : As soon as possible until 30th April 2018
> Location : Oxford Road, Manchester
> Salary : £30,738 to £37,768 per annum
> Hours per week : Full Time
> Further information/to apply:
> https://www.jobs.manchester.ac.uk/displayjob.aspx?jobid=11899
>
> The Centre for Health Informatics is seeking a research associate in
> health data science to drive innovation at the interface between
> biostatistics, machine learning, and software engineering. Working
> with clinical, IT and healthcare data experts you will drive the
> modelling of health data using state-of-the-art methods from
> biostatistics and machine learning.
>
> Ideally, you will have previous relevant experience in academia or
> industry encompassing a strong mathematical element and significant
> statistical knowledge. Ideally, you will be educated to PhD level in
> Statistics, Machine Learning, or related fields; have experience in
> the analysis of complex healthcare problems and datasets; and have
> significant statistical modelling and programming experience. If you
> do not have a PhD you will be considered if you have significant
> demonstrable research and/or industry experience and are in possession
> of a relevant higher degree. Domain knowledge of statistical and
> machine learning analyses of health, biological or social datasets;
> epidemiological models and complex causal inference; and multi-level
> regression are highly sought.
>
> Safe Prescribing of Medication
>
> Certain medications may be contra-indicated in given populations, or
> may require high levels of patient monitoring when used. Examples of
> such hazards: prescribing beta blockers to patients with asthma, or
> failure to test thyroid function regularly in patients receiving
> amiodarone. Using linked primary and secondary data, we have developed
> a tool to identify such medication safety hazards. We would like to
> evaluate the potential impact of such hazards: i.e. if the rate of
> such hazards were reduced, how many medication-related hospital
> admissions could be prevented? We are also developing and evaluating
> interventions (such as dashboards) aiming to reduce the prevalence of
> these hazards.
>
> Dynamic and Longitudinal Approaches to Predictive Modelling
>
> Clinical predictive models are used across healthcare to aid in
> decision making, planning and audit; these are often based on simple
> logistic regression models using information about a patient at a
> fixed point in time. We are developing methodology to exploit the
> richer sources of data that are now available to us. For example:
>
> Utilising longitudinal biomarker and risk factor information: can a
> model be improved by using the full history of risk factor changes
> over time?
> Responding to emerging data in an on-line fashion: as health data
> becomes more ‘connected’ can our models respond dynamically to
> emerging trends in outcome rates, policy changes or secular trends?
> Multiple outcomes and comorbidities: can we build joint, integrated
> models that consider multiple outcomes simultaneously (e.g. stroke,
> heart attack, onset of diabetes)?
>
> The School of Health Sciences is committed to promoting equality and
> diversity, including the Athena SWAN charter for promoting women’s
> careers in STEMM subjects (science, technology, engineering,
> mathematics and medicine) in higher education. The [School/Institute]
> received a Bronze Award in 2013 for their commitment to the
> representation of women in the workplace and we particularly welcome
> applications from women for this post. Appointment will always be made
> on merit. For further information, please visit
> http://www.mhs.manchester.ac.uk/about-us/athena/.
>
> Please note that we are unable to respond to enquiries, accept CV's or
> applications from Recruitment Agencies
>
> Enquiries about the vacancy, shortlisting and interviews:
>
> Matthew Sperrin, Lecturer in Health Data Science
>
> Email: [log in to unmask]
>
> Tel: 0161 306 7629
>
> General enquiries:
>
> Email: [log in to unmask]
>
> Tel: 0161 275 4499
>
> Technical support:
>
> Email: [log in to unmask]
>
> Tel: 01565 818 234
>
>
> Further information and to apply:
> https://www.jobs.manchester.ac.uk/displayjob.aspx?jobid=11899
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