Are you an ambitious researcher looking for your next challenge? Do you have an established background in Machine Learning or Biophysics? Do you want to further your career in one of the UK’s leading research intensive Universities?

We are looking for a Research Fellow in AI and Biophysics to join our team, where we analyse big longitudinal datasets to describe the dynamic in complex systems. Funded by Kidney Research UK (KRUK), you will assist us in describing the transition between healthy and diseased states in Acute Kidney Injury (AKI) patients, using a longitudinal EHR dataset. We were 2022 KRUK MedTech competition winners and been awarded a KRUK Project Grant to conduct internal and external validation of an algorithm we developed, and to deliver a real-time study to provide evidence for NHS adoption. The post-holder will combine the advantages of the original optimal reaction coordinate framework and state-of-the-art machine learning approaches for the analysis of disease dynamics in the longitudinal datasets. 

Acute Kidney Injury (AKI) is characterised by abrupt deterioration in kidney function, usually caused by acute illness. One in five emergency hospital admissions develop AKI, and it is 100 times deadlier than MRSA infection, with NHS treatment costs of ~£1bn/year. The latest data suggest AKI incidence is increasing, while systemic prevention and treatment deficiencies are well documented. There is a clinical need to predict AKI in advance so clinicians can intervene early. In the era of personalised medicine, machine learning techniques are well-suited to such a problem.   

You will work closely with Dr Sergei Krivov and Dr Stefan Auer from the University of Leeds, and Dr Andrew Lewington from the Leeds Teaching Hospital Trust. You should have a PhD (or close to completion) in (Bio/Chemical)-Physics, Applied Mathematics, or a closely related discipline, with experience in state-of-the-art data analysis and Machine Learning methods. Experience in analysis of longitudinal data sets and/or clinical datasets is required. Familiarity with the optimal reaction coordinate framework, which is used for the analysis, is desirable. The ideal candidate will be inquisitive, driven, skilled at solving complex problems, have strong communication skills, and have an interest in the application of physics-based models to a healthcare setting. 

To explore the post further or for any queries you may have, please contact: 

Dr. Sergei Krivov, RCUK Academic Research Fellow

Tel: +44 (0)113 343 3141

Email: S.Krivov@leeds.ac.uk