Clinical Research Fellow in Health Data Sciences and Real World Evidence

<div xmlns="http://www.w3.org/1999/xhtml"> <p></p><p>We are seeking to appoint a highly qualified and dedicated Clinical Research Fellow in Health Data Sciences and Real World Evidence to join the Health Data Sciences research groups led by Professor Daniel Prieto-Alhambra at the Botnar Research Centre, Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences (NDORMS), Oxford. You will join an outstanding, multi-disciplinary and friendly Group of motivated and cutting-edge researchers and you will contribute to clinical research by providing specialist clinical insight and support to our dedicated team.</p><br> <p>As a Clinical Research Fellow in Health Data Sciences and Real World Evidence you will be leading the clinical phenotyping of exposures (e.g. use of a medicinal product or undergoing a specific surgical procedure) and outcomes (e.g. health events) in real world data from the UK and international data sources. You will generate clinical descriptions based on existing medical knowledge, handbooks and/or online resources while maintaining quality control of this process at the same time. You will write research papers for submission to peer-reviewed journals, reports for submission to funders, and abstracts for submission to national and international conferences, participate in group seminars and other departmental activities and provide clinical advice to members of the multi-disciplinary team regarding research studies on the group&#8217;s portfolio.</p><br> <p>You will have a degree in Medicine and a Full General Medical Council Registration. You will be able to write clinical descriptions based on existing medical knowledge together with good ability to write up study reports, manuscripts, and abstracts. Knowledge of R (or other statistical package) programming, interest in academic clinical or health data science research and strong attention to detail are essential. Understanding of real world data governance and methods, knowledge of the OMOP Common Data Model and a good track record of publications are desirable.</p><br> <p>This is a full-time (part-time at 70% or more FTE will be considered) fixed-term appointment for 12 months.</p><br> <p>The closing date for this position is 12 noon on Friday 3 November 2023. You will be required to upload a CV and supporting statement as part of your online application.</p> </div>