Join to apply for the Research Fellow (Health Data Science) role at University of Leeds
Join to apply for the Research Fellow (Health Data Science) role at University of Leeds
THIS ROLE IS OPEN TO INTERNAL CANDIDATES WHO ARE CURRENTLY WORKING IN LICTR ONLY
You will work on the Blood Cancer UK funded project: Blood cancer Long-term follow-up using Integrated Health Systems data (BLISS). You will be based in the Cancer Division at the Clinical Trials Research Unit within Leeds Institute of Clinical Trials Research (LICTR). The Unit is one of the largest in the UK and conducts national and international randomised and early phase clinical trials in a variety of clinical fields. Our main aim is to develop and support novel and efficient trials to change clinical practice for the better. Our past results and current research have already helped to do this. Our results inform the academic development of this specialised field of clinical research on a national and international level. Particularly, we specialise in the development, optimisation and evaluation of complex interventions, complex phase III trials and efficient phase I/II trials, as well as adaptive designs, seamless phase II/III designs and biomarker driven designs.
LICTR hosts a dedicated, cross-cutting Methodological Research, Education and Innovation (M-REI) Division, conducting statistical, trial conduct and mixed methods research inspired by real problems encountered in complex trials.
This is a unique opportunity to join a strategically important research group. You will be a highly talented, inquisitive and enthusiastic individual with a good understanding of medical statistics and considerable applied experience in clinical or healthcare research. You will be interested in pursuing a career in medical statistics and/or health data sciences and will work on the Blood Cancer UK funded project: Blood cancer Long-term follow-up using Integrated Health Systems data (BLISS), to provide guidance on the implementation of processes to integrate routine datasets into blood cancer clinical trials.
You will be responsible for reviewing literature, proposing statistical designs and their associated analysis and implementing these using appropriate statistical software. You will have the opportunity to contribute to teaching and supervise MSc and/or PhD students.
You will join a large, well-established group of statisticians working with other quantitative and qualitative researchers in one of the largest academic clinical trials units in the UK. You will work closely and collaboratively with a multidisciplinary project team, including statisticians, programmers, clinicians, health economists, health service researchers, data managers and trial managers.
What We Offer In Return
26 days holiday plus approx.16 Bank Holidays/days that the University is closed by custom (including Christmas) – That’s 42 days a year!
Generous pension scheme plus life assurance– the University contributes 14.5% of salary[NM1]
Health and Wellbeing: Discounted staff membership options at The Edge, our state-of-the-art Campus gym, with a pool, sauna, climbing wall, cycle circuit, and sports halls.
Personal Development: Access to courses run by our Organisational Development & Professional Learning team.
Access to on-site childcare, shopping discounts and travel schemes are also available.
And much more!
Please note that this post may be suitable for sponsorship under the Skilled Worker visa route but first-time applicants might need to qualify for salary concessions. For more information please visit: www.gov.uk/skilled-worker-visa.
For research and academic posts, we will consider eligibility under the Global Talent visa. For more information please visit: https://www.gov.uk/global-talent
To explore the post further or for any queries you may have, please contact:
Lesley Smith, Senior Research Fellow in Data Science
Email: l.f.smith@leeds.ac.uk
Further details:
* Candidate Brief
Seniority level
* Seniority level
Mid-Senior level
Employment type
* Employment type
Full-time
Job function
* Job function
Engineering and Information Technology
* Industries
Higher Education
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