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Position: Research Fellow
Institution: University College London
Department: Institiute of Cardiovascular Science - MRC Unit for LIfelong Health and Ageing at UCL
Location: London, United Kingdom
Duties: As a Research Fellow, you will form part of a large multidisciplinary team between other universities, with the aim to conduct large-scale epidemiological analyses of long COVID in data collected through the COVID Symptom Study App and several longitudinal population-based cohort studies. The candidate is expected to have a wealth of past experience of research using large scale complex databases as well as longitudinal studies conducted within population-based cohorts. Experience of modelling complex datasets, including time series, genetic, imaging, or -omics data is desirable but not essential
Requirements: PhD or equivalent work experience in data science, machine learning, epidemiology, biostatistics, bioinformatics or a related population health science; Evidence of proficiency in relevant computer packages for quantitative data analyses (e.g. Python, R); Excellent analytic skills, including statistical analysis and data handling; Experience of publishing empirical research findings
   
Text: Research Fellow, - Ref:1877469 Click here to go back to search results Apply Now UCL Department / Division Institiute of Cardiovascular Science Specific unit / Sub department MRC Unit for LIfelong Health and Ageing at UCL Location of position London Grade 7 Hours Full Time Salary (inclusive of London allowance) £36,028 to £43,533 per annum Duties and Responsibilities The COVID Symptom study app was launched in March 2020 to assist health planners manage the pandemic and has collected data of more than 4 million contributors with more than 150 million reports of symptoms and their evolution over time. Complementarily, the MRC LHA at UCL is overseeing a large NIHR-funded grant to investigate the characteristics, risk factors, and health and economic consequences of long COVID, a constellation of long-term health complications that is thought to affect 5% or more of individuals that have suffered from symptomatic COVID-19. As a Research Fellow, you will form part of a large multidisciplinary team between other universities, with the aim to conduct large-scale epidemiological analyses of long COVID in data collected through the COVID Symptom Study App and several longitudinal population-based cohort studies. The candidate is expected to have a wealth of past experience of research using large scale complex databases as well as longitudinal studies conducted within population-based cohorts. Experience of modelling complex datasets, including time series, genetic, imaging, or -omics data is desirable but not essential. For more information, please see the full job description. Key Requirements PhD or equivalent work experience in data science, machine learning, epidemiology, biostatistics, bioinformatics or a related population health science. Evidence of proficiency in relevant computer packages for quantitative data analyses (e.g. Python, R). Excellent analytic skills, including statistical analysis and data handling. Experience of publishing empirical research findings. Further Details A job description and person specification can be accessed at the bottom of this page. To apply please click the "Apply Now" button below. If you have any queries regarding the vacancy please contact Jane Johnson, jane.johnson@ucl.ac.uk or the application process, please contact Jane Johnson, jane.johnson@ucl.ac.uk (020 7670 5700) UCL Taking Action for Equality Closing Date 5 Aug 2021 Latest time for the submission of applications 23:59 Interview date TBC Our department holds an Athena SWAN Silver award, in recognition of our commitment and demonstrable impact in advancing gender equality. This appointment is subject to UCL Terms and Conditions of Service for Research and Support Staff. Please use these links to find out more about UCL working life including the benefits we offer and UCL Terms and Conditions related to this job. Job Description Apply Now
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