Research Associate in Multiphysics-Informed Machine learning for Cardiovascular Biomechanics

Join to apply for the Research Associate in Multiphysics-Informed Machine learning for Cardiovascular Biomechanics role at The University of Manchester

Research Associate in Multiphysics-Informed Machine learning for Cardiovascular Biomechanics

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Join to apply for the Research Associate in Multiphysics-Informed Machine learning for Cardiovascular Biomechanics role at The University of Manchester

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Join our dynamic, multidisciplinary team as a Research Associate and make a transformative impact in scientific machine learning and digital twins for healthcare innovation! This role focuses on developing high-fidelity cardiovascular models, simulating fluid dynamics, and exploring device-flow and device-tissue interactions through cutting-edge multi-physics and physiological modelling.

You will leverage clinical and experimental datasets, apply physics-based simulations, and harness the power of scientific machine learning, including data assimilation and uncertainty quantification. Additionally, you'll work with large-scale multimodal datasets, clinical trials data, and population imaging studies to drive innovation in personalised medicine and in silico trials.

If you're passionate about pushing the boundaries of computational modelling and shaping the future of digital healthcare, this is the opportunity to bring your expertise to life!

What You’ll Need

Applicants should have a PhD in computational cardiovascular mechanics and prosthetic valves or related fields and expertise in either computational solid mechanics to analyse soft-tissue deformations and device interactions or computational fluid mechanics to enable analysis of haemodynamics and thrombosis. One of the critical challenges we want to tackle is how to efficiently execute ensembles of virtual experiments entailing. Experience in working with multiphysics and multiscale models and in accelerated methods for solving partial differential equations and scientific machine learning (physics-informed machine learning) is essential. A developing publication profile will be advantageous.

As this role involves research at a postgraduate level, applicants who are not an EEA national or a national of an exempt country and who will require sponsorship under the Skilled Worker route of the UK Visas and Immigration’s (UKVI) Points Based System in order to take up the role, will be required to apply for an Academic Technology Approval Scheme (ATAS) Certificate and will need to obtain this prior to making any official visa application UKVI.

What you will get in return:

• Fantastic market leading Pension scheme
• Excellent employee health and wellbeing services including an Employee Assistance Programme
• Exceptional starting annual leave entitlement, plus bank holidays
• Additional paid closure over the Christmas period
• Local and national discounts at a range of major retailers
  
  
  

Seniority level

• Seniority level

  Entry level

Employment type

• Employment type

  Full-time

Job function

• Job function

  Other

• Industries

  Higher Education

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