Research Fellow in Health Data Science
Are you a machine learning researcher who is interested in improving the
healthcare system? Do you have experience with large, complex datasets, and in
training large neural networks on GPU-enabled systems? Do you want to push the
boundaries of what is possible with NHS data, and help GPs and pharmacists to
improve people's health?
The DynAIRx project aims to develop new, easy to use, artificial intelligence
(AI) and data visualization tools that help GPs & pharmacists treat patients
who are living with multimorbidity (two or more long-term health
conditions). The project is large (consisting of the Universities of Leeds,
Liverpool, Manchester and Glasgow, and three NHS organisations), cross-
disciplinary (clinicians, computer scientists, statisticians), and is funded
by the National Institute for Health Research.
DynAIRx is focussing on groups of patients who are at high risk of rapidly
worsening health from their multimorbidity and, due to taking multiple
medicines (polypharmacy), have increased likelihood of developing serious
side effects. Our new tools will combine information from electronic health
and social care records, clinical guidelines and risk-prediction models to
ensure that clinicians and patients have the best information to prioritise
and support Structured Medication Reviews and, therefore, reduce the risk that
those patients are prescribed potentially harmful combinations of drugs.
The University of Leeds team comprises Professor Roy Ruddle (data
visualization), Professor Andrew Clegg (clinician and specialist in
geriatric medicine), Dr Samuel Relton (machine learning, statistics) and
three research fellows. As the Research Fellow in Health Data Science, you
will take responsibility for training AI models, based on temporal graphs, to
predict patient outcomes, find indicative patterns of care, and create risk
trajectories to help guide clinical decision making. The key parts of your
research in the DynAIRx project will be: (a) co-designing a thorough
analysis plan including model architecture, hyper-parameter optimisation, and
performance testing, (b) implementing an effective approach using Python on
a cloud service, (c) evaluating the solution with other researchers and
users (GPs and pharmacists), and (d) writing documentation and training
material for users.
Holding a PhD (or close to completion) in Computing, Mathematics or a
closely allied discipline, or have equivalent experience, you will have
programming skills and evidence of the ability to develop machine learning
models. Some travel to UK study sites will be required.
To explore the post further or for any queries you may have, please
Dr Samuel Relton, Senior Research Fellow
Tel: +44 (0)113 343 6731
As an international research-intensive university, we welcome students and
staff from all walks of life and from across the world. We foster an inclusive
environment where all can flourish and prosper, and we are proud of our strong
commitment to student education. Within the Faculty/School of Medicine we are
dedicated to diversifying our community and we welcome the unique
contributions that individuals can bring, and particularly encourage
applications from, but not limited to Black, Asian, people who belong to a
minority ethnic community, people who identify as LGBT+; and disabled people.
Candidates will always be selected based on merit and ability.
Location: Leeds - Main Campus
Faculty/Service: Faculty of Medicine & Health
School/Institute: Leeds Institute of Health Sciences (LIHS)
Grade: Grade 7
Salary: £35,333 to £42,155 p.a.
Working Time: 1.0 Full time Equivalent
Post Type: Full Time
Contract Type: Fixed Term (Available for 30 months due to external
Release Date: Wednesday 31 August 2022
Closing Date: Wednesday 28 September 2022
Interview Date: To be confirmed
Downloads: Candidate Brief