Post-Doctoral Research Associate in Cardiovascular Virtual Twins II

at  Kings College London

Job id: 095947. Salary: £43,205 - £46,732 per annum, including London Weighting Allowance.
Posted: 27 September 2024. Closing date: 27 October 2024.
Business unit: Faculty of Life Sciences & Medicine. Department: Biomedical Engineering.
Contact details: Jordi Alastruey. jordi.alastruey-arimon@kcl.ac.uk
Location: St Thomas’ Campus. Category: Research.
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About us
A post-doctoral research associate position is available at King’s College London (KCL), funded by the European Commission Horizon 2023 project Virtual Twins as tools for Personalised Clinical Care (VITAL) ( https://vital-horizoneurope.eu). KCL, a world-leading research university over 200 years of heritage, is participating in VITAL by engaging the School of Biomedical Engineering & Imaging Sciences, a cutting-edge research and teaching institution located in St Thomas’ Hospital. The school is dedicated to the development, clinical translation, and clinical application of medical imaging and computational modelling technologies.
KCL’s contribution to VITAL will primarily focus on the computational modelling work packages of the project, leveraging our expertise in reduced-order blood flow modelling ( http://haemod.uk/nektar) and haemodynamic signal analysis ( http://haemod.uk/PWA), and the creation and testing of large-scale in silico pulse wave datasets ( http://haemod.uk/virtual-database).
About the role
The post holder will contribute to the development and clinical validation of the VITAL digital twins for personalised cardiovascular care. These advanced models aim to predict disease progression and optimise patient management strategies, surpassing current clinical standards. VITAL’s virtual twins will be used to study four complex circulation overload disorders – systemic hypertension, heart failure, and hemodynamically complicated atrial septal defects – examining the interplay between cardiac and vascular function, renal and hormonal influences, and various environmental and genetic factors. The technology, developed with input from healthcare professionals, will be validated in over 200 patients across five clinical studies in France and the UK.
The post-holder will contribute to the personalisation of the virtual twins by developing pipelines (including machine learning models) for structural and functional personalisation and creating virtual patient cohorts for each circulation overload disorder to preclinically validate the virtual human twins. The role involves close collaboration with modelling experts from the Universities of Auckland, MaastrichtDelft, EPFL, and other industrial and clinical partners. The codes developed by VITAL will be linked to EDITH, an ecosystem for digital twins in healthcare. The post-holder will work under the supervision of Professor Philip Chowienczyk (clinical advisor), and Drs Peter Charlton (machine learning advisor) and Jordi Alastruey (modelling advisor), with the expectation to publish in high-impact journals and present findings at international conferences.
This is a full-time post (100% full time equivalent), and you will be offered a fixed term contract for 2.5 years.
About you
To be successful in this role, we are looking for candidates to have the following skills and experience:

Essential criteria

• PhD awarded, or near completion*, in biomedical engineering, physics, mathematics, computer science, or a related subject.
• First or second class honors degree in biomedical engineering, physics, mathematics, computer science, or a related subject.
• Advanced programming skills in Python, Matlab or C++, as well as machine learning/deep learning frameworks such as Pytorch or Tensorflow
• Good writing and presentation skills.
• A solid research background in a relevant field, supported by published peer-reviewed work.
• Ability to work independently and as part of a team.
• Ability to work and communicate effectively with people from a wide range of disciplines and organisations.
• An enthusiastic attitude and scientific approach to solving research problems.

Desirable criteria

• Knowledge of cardiovascular physiology
• Knowledge of signal and image processing techniques
• Experience with student supervision
• Experience in multidisciplinary research
• Evidence of leading own initiatives/projects

Downloading a copy of our Job Description
Full details of the role and the skills, knowledge and experience required can be found in the Job Description document, provided at the bottom of the next page after you click “

Please refer the Job description for details