Connecting to Linkedin

Lead Research Engineer

This is a chance to work for an award-winning medical imaging company who are creating software to help predict the risk of heart attacks and thus revolutionise the management of cardiovascular disease.  They are deploying a cloud-based product to provide analysis to millions of patients world-wide and are looking for a Lead Research Engineer to join their rapidly growing R&D team.
Key Responsibilities:
Lead a small team of research scientists working on image segmentation and quantification of Coronary CTA images and prognostic risk models using classic and machine learning techniques
Define research strategy and roadmap in fulfilment of product roadmap
Provide mentoring and coaching of the research team members
Ensure algorithms and research results can be transitioned efficiently to the product code-base
Hands-on development of algorithms for CCTA image segmentation, quantification, rendering
Hands-on development / testing of prognostic risk models
Oversee and participate in algorithm validation and robust recording of studies, methodology and results
Oversee development of a robust framework for systematic, reproducible deep learning encompassing data preparation and annotation, training, validation cycles
Oversee and participate in generation of IP and patents related to core technologies
Work with external collaborators on research projects
Identify opportunities for and oversee / participate in writing scientific publications
Skills and Experience (required)
Excellent communication skills and positive outcome-oriented attitude
5 years experience working in a regulated industry such as medical device development
4 years experience leading a commercial research team
4 years experience with deep learning
4 years experience using Python in a commercial research environment
2 years experience in CT image segmentation techniques
Working knowledge of DICOM
Line management experience
PhD degree in technical medical imaging discipline
Experience in CCTA vessel analysis, rendering and annotation techniques
Experience in CT quantification / QIBA
Basic knowledge in biostatistics including regression modelling and clinical prediction models
Hand-on experience with TensorFlow and Keras
Experience with data management and collaboration partner management
Experience with Linux