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Position: Research Fellow in Data Analysis and Machine Learning
Institution: University College London
Department: EGA Institute for Womens Health - Department of Womens Cancer
Location: London, United Kingdom
Duties: The post-holder will contribute the ACED-funded research project “Dynamic predictive model for baseline early detection and follow-up re-evaluation of the risk of prostate cancer progression on active surveillance (PROGRESS Prostate)” aimed at developing a personalised dynamic predictive model able to estimate the risk of prostate cancer progression throughout the active surveillance continuum starting from the initial appointment. A significant part of the work will be devoted to the application of various machine learning algorithms to the analysis of biomedical data
Requirements: Applicants must have a higher degree in a relevant discipline at a PhD level (if degree has not yet been granted, the final accepted version of the thesis should have been submitted to the degree granting university by the time of starting) as well as the necessary experience and skills to carry out the work, particularly programming and running of numerical simulations in R and Python in relation to the ACED project including the development and application of novel modelling approaches
   
Text: Research Fellow in Data Analysis and Machine Learning, - Ref:1879863 Click here to go back to search results Apply Now UCL Department / Division EGA Institute for Womens Health Specific unit / Sub department Department of Womens Cancer Location of position London Grade 7 Hours Full Time Salary (inclusive of London allowance) £36,770 - £39,843 per annum Duties and Responsibilities Applicants are invited for a Post-doctoral Research Fellow position in an exciting research programme held at the Institute for Womens Health, UCL. The post-holder will be based within the UCL Department of Mathematics and the Department of Womens Cancer at the UCL Institute for Womens Health. Our groups use different machine learning algorithms, including longitudinal, network and Deep Learning methods to analyse proteomic, genetic, epigenetic and other multidimensional clinical and epidemiological data to identify and test potential biomarkers for the detection of diseases. The post-holder will contribute the ACED-funded research project Dynamic predictive model for baseline early detection and follow-up re-evaluation of the risk of prostate cancer progression on active surveillance (PROGRESS Prostate) aimed at developing a personalised dynamic predictive model able to estimate the risk of prostate cancer progression throughout the active surveillance continuum starting from the initial appointment. A significant part of the work will be devoted to the application of various machine learning algorithms to the analysis of biomedical data. This post is funded until 31 August 2022 (100% FTE) in the first instance. Key Requirements Applicants must have a higher degree in a relevant discipline at a PhD level (if degree has not yet been granted, the final accepted version of the thesis should have been submitted to the degree granting university by the time of starting) as well as the necessary experience and skills to carry out the work, particularly programming and running of numerical simulations in R and Python in relation to the ACED project including the development and application of novel modelling approaches. They must have technical knowledge of the methods to be used, have excellent communication skills to be able to effectively present work to colleagues and be able to work within a team. They must be organised and have a meticulous attention to detail both experimentally and with record keeping. They must be self-motivated and have the initiative to work independently. Further Details A job description and person specification can be accessed at the bottom of this page. To apply for the vacancy please click on the Apply Now button below. If you have any queries regarding the vacancy or the application process, please contact Christina Ahlfors (c.ahlfors@ucl.ac.uk). UCL Taking Action for Equality We will consider applications to work on a part-time, flexible and job share basis wherever possible. Closing Date 13 Dec 2021 Latest time for the submission of applications 23:59 Interview date TBC Our department holds an Athena SWAN Gold award, in recognition of our long-term commitment and 'beacon' status 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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