Research Associate of Computational Fluid Dynamics of Blood
| Posting date: | 17 March 2026 |
|---|---|
| Hours: | Full time |
| Closing date: | 14 April 2026 |
| Location: | Sheffield, S10 2TN |
| Company: | University of Sheffield |
| Job type: | Temporary |
| Job reference: | 2379-44043328 |
Summary
University of Sheffield
Working Pattern:
Grade 7 (Full time, 100%FTE)
Faculty: (if applicable)
Faculty of Engineering
School:
School of Mechanical, Aerospace and Civil Engineering
Are you a passionate expert in computational fluid dynamics looking to apply your numerical skills to a transformative healthcare challenge? We have an exciting opportunity in the School of Mechanical, Aerospace and Civil Engineering for a Research Associate to use their skills in computational fluid mechanics to revolutionize haemodialysis and make a profound impact on global health.
The Opportunity
You will join the Sheffield Fluid Mechanics Group, working closely with experts across Engineering, the Medical School, and Sheffield Teaching Hospitals. As a key member of this interdisciplinary project, you will lead the computational characterization of a novel, disruptive dialyser design. Directly supervised by Professor Pierre Ricco, you will be responsible for the theoretical analysis and numerical simulations of flows and passive transport in a newly designed dialyser. You will utilize OpenFoam as your main computational tool.
This role offers a unique opportunity to bridge fundamental engineering principles with real-world clinical applications. You will work in close collaboration with two other Research Associates focusing on experimental verification and clinical validation, contributing directly to a greener, more efficient future for nephrology.
About You
We are looking for a motivated researcher who thrives in collaborative, interdisciplinary environments. You should hold a PhD (or be nearing completion) in Computational Fluid Mechanics, or a closely related field. You will bring:
● Significant experience with numerical simulation of blood and dialysate flows and of the transport of passive scalar.
● The ability to develop software and troubleshoot complex numerical software for the simulation of flows in the dialyser.
● Strong data analysis skills for post-processing large datasets.
● A passion for interdisciplinary research and clinical impact.
What We Offer:
The University of Sheffield is a remarkable place to work. Our people are at the heart of everything we do. Their diverse backgrounds, abilities and beliefs make Sheffield a world-class university.
We offer a fantastic range of benefits including a highly competitive annual leave entitlement (with the ability to purchase more), a generous pensions scheme, flexible working opportunities, a commitment to your development and wellbeing, a wide range of retail discounts, and much more.
Find out more at sheffield.ac.uk/jobs/benefits and join us to become part of something special.
We build teams of people from different heritages and lifestyles from across the world, whose talent and contributions complement each other to greatest effect. We believe diversity in all its forms delivers greater impact through research, teaching and student experience.
Working Pattern:
Grade 7 (Full time, 100%FTE)
Faculty: (if applicable)
Faculty of Engineering
School:
School of Mechanical, Aerospace and Civil Engineering
Are you a passionate expert in computational fluid dynamics looking to apply your numerical skills to a transformative healthcare challenge? We have an exciting opportunity in the School of Mechanical, Aerospace and Civil Engineering for a Research Associate to use their skills in computational fluid mechanics to revolutionize haemodialysis and make a profound impact on global health.
The Opportunity
You will join the Sheffield Fluid Mechanics Group, working closely with experts across Engineering, the Medical School, and Sheffield Teaching Hospitals. As a key member of this interdisciplinary project, you will lead the computational characterization of a novel, disruptive dialyser design. Directly supervised by Professor Pierre Ricco, you will be responsible for the theoretical analysis and numerical simulations of flows and passive transport in a newly designed dialyser. You will utilize OpenFoam as your main computational tool.
This role offers a unique opportunity to bridge fundamental engineering principles with real-world clinical applications. You will work in close collaboration with two other Research Associates focusing on experimental verification and clinical validation, contributing directly to a greener, more efficient future for nephrology.
About You
We are looking for a motivated researcher who thrives in collaborative, interdisciplinary environments. You should hold a PhD (or be nearing completion) in Computational Fluid Mechanics, or a closely related field. You will bring:
● Significant experience with numerical simulation of blood and dialysate flows and of the transport of passive scalar.
● The ability to develop software and troubleshoot complex numerical software for the simulation of flows in the dialyser.
● Strong data analysis skills for post-processing large datasets.
● A passion for interdisciplinary research and clinical impact.
What We Offer:
The University of Sheffield is a remarkable place to work. Our people are at the heart of everything we do. Their diverse backgrounds, abilities and beliefs make Sheffield a world-class university.
We offer a fantastic range of benefits including a highly competitive annual leave entitlement (with the ability to purchase more), a generous pensions scheme, flexible working opportunities, a commitment to your development and wellbeing, a wide range of retail discounts, and much more.
Find out more at sheffield.ac.uk/jobs/benefits and join us to become part of something special.
We build teams of people from different heritages and lifestyles from across the world, whose talent and contributions complement each other to greatest effect. We believe diversity in all its forms delivers greater impact through research, teaching and student experience.