Research Associate in Biostatistics and Machine Learning

We are seeking a talented postdoctoral researcher with a background in biostatistics and machine learning as part of a newly-funded BHF Professorship in Cardiovascular AI. You will be part of a multidisciplinary team to develop cluster analysis and risk prediction algorithms for heart disease from high dimensional clinical datasets - including motion phenotypes derived from biomedical imaging and genetic markers of disease.

You will be developing novel algorithms for time-to-event analyses, clustering of high dimensional data, and causal inference in complex systems.

The successful candidate will be able to develop creative solutions to challenging biomedical problems that use large scale imaging, outcome and genomic datasets. Experience of high-performance computing would be an advantage including use of the DNAnexus platform.

A strong background in biostatistical modelling is required with excellent coding skills in R and Python. Prior experience of developing and testing machine learning algorithms for prediction tasks using multimodal data would be an advantage – including generative and foundation modelling. You will have a track record of published research outputs – including software and/or datasets.

Please note that job descriptions are not exhaustive, and you may be asked to take on additional duties that align with the key responsibilities mentioned above.