The Machines and the Heart: Using AI to predict cardiovascular risks from CT images

Description

This is a funded PhD position in applied medical imaging and deep learning to heart diseases, suited to candidates with an applied mathematics, computer science, electrical engineering, medical imaging, biomedical engineering, physics or equivalent MSc/BSc degree.

The overarching aim of this project is to develop new Artificial Intelligence prediction tools to automatically identify and quantify Coronary Artery Calcification on CT images. Cardiovascular diseases (CVDs) are diseases of the heart and blood vessels. According to the World Health Organisation (WHO), 17.9 million people die each year from CVDs, an estimated 31% of all deaths worldwide. Large amounts of data in various forms. The successful candidate will develop novel deep-learning image analysis solutions to support the prediction of CVDs based on an unprecedented large image dataset in Liverpool.

The successful PhD candidate will benefit from working with a multidisciplinary team in which there exists extensive experience in the areas of computer science, image processing, high performance computing, mathematics, and medicine. All postgraduate students undertake the PGR Development Programme which aims to enhance their skills for a successful research experience and career. They are required to maintain an online record of their progress and record their personal and professional development throughout their research degree. The 1st Year Development Workshops encourage inter- and cross-disciplinary thinking and identify and develop the knowledge, skills, behaviours and personal qualities that all students require. In the 2nd year all students take part in a Poster Day to provide an opportunity to present their research to a degree educated general public, and in the 3rd year students complete a career development module. Other online training, such as ‘Managing your supervisor’ and ‘Thesis writing’ is provided centrally.

Any specific eligibility requirements:   The successful candidate should have, or expect to have an Honours Degree at 2.1 or above (or equivalent) in Mathematics, Engineering, Physics or Computer Science. It is essential to have good background knowledge in mathematics, machine learning, computer programming (e.g., Python or C++), and signal/image processing plus a proactive approach to their work. Candidates whose first language is not English should have an IELTS score of 6.5 (with no band below 5.5) or equivalent.