Machine Medicine Technologies (MMT) uses computer vision and computational statistics to enhance the neurological assessment of patients. Their first product, KELVIN-PD, allows motor assessments in Parkinson’s disease to be performed, recorded and used both faster and better than has ever previously been possible. KELVIN-PD is already in use at multiple clinical sites and already possesses a CE mark, being a class I medical device. MMT aims to build the platform into a generalised tool for patient selection, surgical planning and device programming for machine brain interfacing, a revolutionary therapeutic innovation. This will require the product to be built to the standards of a class III medical device.
You are an ambitious and capable PhD research scientist in computational statistics and machine learning, with a solid publication record and several years post-PhD experience in industrial or academic labs. You are conversant with the relevant branches of mathematical statistics, including Bayesian inference, MCMC and time series analysis. Previous work in a biomedical sector would be beneficial but not mandatory. You care about impact and want to do work that matters.