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.
Working in a vibrant team at the intersection of algorithm development and clinical sciences with the aim to improve the treatment of patients with heart disease.Grow your career with tailored training programmes for academic staff including dedicated support with navigating your career and managing research as well as a transparent promotion process.Sector-leading salary and remuneration package (including 39 days off a year and generous pension schemes)Be part of a diverse, inclusive, and collaborative work culture with various and resources designed to support your personal and professional .
