We are seeking a Research Associate to join the School of Mechanical, Aerospace and Civil Engineering (MAC) and the INSIGNEO Institute for in silico medicine at the University of Sheffield. This 42-month position is funded by the Engineering and Physical Sciences Research Council (EPSRC) as part of the pioneering project "Vascular Computational Modelling in Chronic Kidney Disease (VASCO-CKD)".
Chronic Kidney Disease (CKD) affects ~10% of the global population and is primarily driven by diabetes and hypertension. The VASCO-CKD project aims to develop the first clinically relevant, comprehensive computational model of renal haemodynamics and validate it using multi-scale human clinical data prospectively collected as part of the project.
As the successful candidate, you will lead the core computational modelling activities within Work Package 1 (WP1), under the direct supervision of Dr. Alberto Marzo. You will work closely with an interdisciplinary team, including a medical physics PDRA focused on clinical imaging data collection (WP2), alongside clinicians, MRI physicists and established public and clinical stakeholder networks (WP3).
You will be embedded within the INSIGNEO Institute, Europe's largest in silico medicine institute, providing a world-class, multidisciplinary ecosystem of over 400 clinical and engineering researchers. This role provides an outstanding opportunity to leverage your numerical modelling skills to make a real-world clinical impact on early disease prediction and personalized medicine.
Main duties and responsibilities
The postholder will lead the numerical model development and computational simulations within Work Package 1 (WP1). Key duties include:
- Lead the extension of Sheffield’s open-source 1D-0D whole-circulation solver (openBF) to incorporate detailed representations of renal macro- and microcirculation, glomerular filtration, and tubular reabsorption.
- Integrate intrinsic renal autoregulation mechanics (myogenic response, tubuloglomerular feedback) and extrinsic baroreflex feedback loops into the circulation framework.
- Construct explicit coupling methods to link the vascular models with state-of-the-art disease progression frameworks.
- Perform global sensitivity analyses (Sobol method) leveraging Gaussian process (GP) emulators to isolate sensitive parameters and optimize workflow computational costs.
- Calibrate and validate computational models against individual and population-level data generated in WP2, maintaining strict alignment with FDA and ASME V&V40 reliability guidelines.
- Generate UK-representative human virtual populations capturing demographic variations (age, sex, ethnicity) and varying stages of diabetic and hypertensive kidney disease.
- Deploy the virtual populations in simulated in silico clinical trials to identify robust non-invasive wave-derived biomarkers for early CKD detection and diagnosis.
- Package data, analysis pipelines, and documented models into a clean, open-access public repository hosted via GitHub and Zenodo under open Apache 2.0 and CC-BY 4.0 licensing.
- Maintain highly effective working relationships across the interdisciplinary team, presenting key milestones at monthly alignment meetings and international conferences.
- Carry out other duties, commensurate with the grade and remit of the post.
Person Specification
Our diverse community of staff and students recognises the unique abilities, backgrounds, and beliefs of all. We foster a culture where everyone feels they belong and is respected. Even if your experience doesn't match perfectly with this role's criteria, your contribution is valuable, and we encourage you to apply. Please ensure that you reference the application criteria in the application statement when you apply.
A PhD (or near completion/equivalent experience) in Engineering, Applied Mathematics, Physics, Computational Biomedical Imaging, or a related highly quantitative field.
Documented experience in numerical model development, mathematical modelling, and programming.
Strong background in fluid mechanics, cardiovascular mechanics, or physiological haemodynamics.
Experience with open-source software codebases, version control (e.g., GitHub), and open-science data sharing standards.
Effective communication and interpersonal skills, with a proven ability to collaborate in multi-disciplinary research environments.
A track record of authoring high-quality peer-reviewed scientific publications or technical project reports.
Experience in performing global sensitivity analyses (e.g., Sobol method) or constructing Gaussian process emulators.
Familiarity with regulatory verification and validation guidelines for clinical software models (e.g., ASME V&V40 or FDA frameworks).
Further Information
Senior Lecturer in Biomechanics
https://sheffield.ac.uk/mac
If you do not currently hold the right to work in the UK, you can find more information here to help determine your visa eligibility. Additional guidance is also available on the UK Visa & Immigration website.
For informal enquiries about this job contact Dr. Alberto Marzo, Senior Lecturer in Biomechanics: on
[email protected] Next steps in the recruitment process
It is anticipated that the selection process will take place on a date to be confirmed following shortlisting. This will consist of a formal panel interview and a technical presentation outlining your previous computational modelling work. We plan to let candidates know if they have progressed to the selection stage shortly after the closing date. If you need any support, equipment or adjustments to enable you to participate in any element of the recruitment process you can contact the HR recruitment team.
Our vision and strategic plan
We are the University of Sheffield. This is our vision: sheffield.ac.uk/vision ().
What we offer
- A minimum of 41 days annual leave including bank holiday and closure days (pro rata) with the ability to purchase more.
- Flexible working opportunities, including hybrid working for some roles.
- Generous pension scheme.
- A wide range of discounts and rewards on shopping, eating out and travel.
- A variety of staff networks, providing opportunities for social interaction, peer support and personal development (for example, Race Equality, LGBT+, Women’s and Parent’s networks).
- Recognition Awards to reward staff who go above and beyond in their role.
- A commitment to your development access to learning and mentoring schemes; integrated with our Academic Career Pathways and the INSIGNEO Training Academy.
- A range of generous family-friendly policies
- paid time off for parenting and caring emergencies
- support for those going through the menopause
- paid time off and support for fertility treatment
- and more
We are a Disability Confident Employer. If you have a disability and meet the essential criteria for this job you will be invited to take part in the next stage of the selection process.
