Research Associate (Fixed Term)

An opportunity has arisen for a talented statistician or probabilistic machine learning methods developer to work with Drs. Robert Goudie, Paul Kirk, and Sach Mukherjee at the MRC Biostatistics Unit, Cambridge University, as part of a unit of work funded as part of the NIHR Cambridge Biomedical Research Centre-funded Devices and Therapies research theme. 

The project forms part of the exciting emerging area of real-time precision medicine, which seeks to provide statistical and machine learning tools to optimise each patient's care in real-time. The post-holder will have the opportunity to contribute to several challenging open problems in the statistical analysis of clinical electronic health record data, including prediction using real-time data with complex endpoints, and tuning prediction according to clinically informed criteria. The post will involve collaboration with ICU physicians involved in day-to-day clinical practice at the general ICU at Addenbrooke's. 

The project is concerned with the development of clinician-in-the-loop AI tools to help direct decisions in secondary care. The post-holder will focus on developing and implementing methods for prediction of the psychiatric disorder delirium in patients in the general Intensive Care Unit (ICU) at Addenbrooke's hospital using Electronic Health Record (EHR) data. Addenbrooke's is an internationally renowned teaching hospital in Cambridge and in 2014 was the first UK hospital to implement Epic's fully electronic health record eHospital system. This provides a single, integrated EHR, with real-time information recorded at the patient's bedside, including observations, blood tests, procedures and medications. Seven years of data have now been collected, providing a large, rich observational clinical informatics dataset. 

We have previously shown that an existing ICU delirium predictive tool cannot meaningfully inform targeting of preventative measures (Crit Care 2020 PMID:32228666), partly due to not accounting for post-admission ICU interventions. We propose to develop a tool that dynamically updates predictions, particularly to account for pharmacological sedation which we expect to be predictive. 

We are seeking an ambitious and motivated individual to contribute to this research. The team has strong collaborations with groups across the Cambridge Biomedical Campus and beyond. 

The successful candidate will have a PhD, ideally statistics or probabilistic machine learning. Experience with biomedical applications would be highly advantageous, but not essential. A desire to address questions of substantive medical importance and disease relevance is essential. Good communication skills and an enthusiasm for collaborating with others are also essential. Strong programming ability would be desirable, and experience working with electronic health record (EHR) data and/or predictive modelling would be advantageous. The successful applicant will be supported in their career development with a range of formal courses and on-the-job training.

For an informal discussion about this post please contact Dr Paul Kirk paul.kirk@mrc-bsu.cam.ac.uk or Dr Robert Goudie robert.goudie@mrc-bsu.cam.ac.uk.

Fixed-term: The funds for this post are available until 31 August 2025 in the first instance.

To apply online for this vacancy and to view further information about the role, please visit: http://www.jobs.cam.ac.uk/job/39381.

Please ensure that you upload a covering letter and a CV in the Upload section of the online application. The covering letter should outline how you match the criteria for the post and why you are applying for this role. If you upload any additional documents which have not been requested, we will not be able to consider these as part of your application.

Please include details of your referees, including email address and phone number, one of which must be your most recent line manager.

The closing date for applications is: 2nd March 2023.

The interview date for the role is: To be confirmed

Please quote reference SL35303 on your application and in any correspondence about this vacancy.

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