About this role
About the role:
Cardiac diffusion tensor imaging (cDTI) is uniquely able to investigate cardiac microstructure noninvasively. It is providing us with a new understanding of microscopic changes behind heart diseases and the mechanisms of cardiac contraction. Imperial and the Royal Brompton Hospital have been at the forefront of advances in cDTI methods which offer potential for translation to the clinic.
A three-year Research Associate or Research Assistant post in Computational Modelling of Cardiac Diffusion Tensor Imaging is available to further these methods. This project forms part of a BHF and EPSRC funded programme underway at Imperial and the Royal Brompton Hospital (RBH) to investigate cardiac microstructure and microvasculature. The person appointed will join the multidisciplinary team, working with supervisors Dr. Andrew Scott (imaging physics), National Heart and Lung Institute/RBH and Professor Denis Doorly (computational modelling), Aeronautics, Imperial. The research will develop our computational modelling of diffusion and perfusion within heart muscle to enable more precise diagnosis of cardiovascular disease. Previous experience of MRI is not essential, though applicants require a strong background in computational and mathematical modelling.
What you would be doing:
You will derive geometric and other parameters from microscopy images, developing microstructural models using AI where appropriate. You will perform theoretical and computational analyses of transport and exchange processes using high performance computing, to guide optimisation of cDTI measurements.
What we are looking for:
Applicants must have:
PhD or equivalent in Physics, Maths, Engineering or closely related field, or nearing completion (research associate). MSc in one of the above subjects (research assistant). Experience in computational modelling of partial differential equations. Programming experience in several languages, e.g.: Matlab, Python, C++. Strong publication record and presentation at scientific meetings. Excellent written and verbal communication. Ability to work unsupervised and within a team. Excellent problem solving skills.
Other desirable criteria include:
Image analysis experience, e.g. automated segmentation and/or image registration. Experience using high performance computing clusters/GPU based parallelisation. Experience solving diffusion or related equations by Monte Carlo methods. Knowledge of MRI/diffusion tensor MRI. Experience with machine learning/deep learning/AI methods.
What we can offer you:
Be part of a team developing cutting-edge simulation technology having real-world impact. Grow your network by being part of a large multi-institutional team of researchers. Opportunities and training to develop and enhance your software engineering skills.
The opportunity to continue your career at a world-leading institution and be part of our mission to continue science for humanity. Grow your career: Gain access to Imperial’s sector-leading dedicated career support for researchers as well as opportunities for promotion and progression Sector-leading salary and remuneration package (including 39 days off a year and generous pension schemes).
Further Information
For further details contact: Professor Denis Doorly (Aeronautics), [email protected] or Dr Andrew Scott (RBH, NHLI) [email protected]
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.
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