Research Fellow in Machine Learning Methods for Digital Epidemiology
Duties and Responsibilities
The Department of Computer Science is looking to recruit a researcher in Machine learning methods for digital epidemiology. This position is funded by the EPSRC project i-sense and an unrestricted Google Gift. The successful candidate should have the desire and ability to: (a) develop machine learning methods for digital/computational epidemiology with a focus on disease prevalence forecasting using neural network architectures that are able to leverage non-medical information such as web search activity, (b) constraint models developed in (a) with priors from established epidemiological approaches (e.g. mechanistic models) in joint optimisation frameworks, (c) publish the outcomes in peer-reviewed conferences or journals, and (d) deploy such models to forecast influenza and COVID-19 rates in the UK (at least) and share outcomes with the UK Health Security Agency.
This post will be funded for 2 years in the first instance.
The successful candidate should have a PhD in a relevant subject (or should at least have submitted their PhD Thesis), with strong experience in machine learning, proven by a strong publication track in related conference venues and journals. Previous experience in health-related topics is a desirable skill.
Appointment at Grade 7 is dependent upon having been awarded a PhD; if this is not the case, initial appointment will be at Research Assistant Grade 6B (salary 32,217 – 33,958 per annum) with payment at Grade 7 being backdated to the date of final submission of the PhD thesis.
A job description and person specification can be accessed at the bottom of this page.
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We will consider applications to work on a part-time, flexible and job share basis wherever possible.
16 Feb 2022
Latest time for the submission of applications
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