Research Associate - Vascular Computational Modelling in Chronic Kidney Disease

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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.

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 Git Hub and Zenodo under open Apache 2.0 and CC-BY 4.0 licensing.
• Maintain highly effective working relationships across the interdisciplinary team, presenting key…