Optimising a microfluidic assay for sepsis diagnostics by combining numerical simulations with machine learning

Improving healthcare is one of the global challenges of our time. Sepsis (a life-threatening organ dysfunction caused by a dysregulated host immune response to infection) is linked to 20% of all deaths in the world. Diagnosing sepsis quickly is of utmost importance to the survival of a patient, as mortality from sepsis increases as much as 8% for every hour that treatment is delayed. The emergence of microfluidics has enabled fascinating opportunities for disease diagnostics. The US-based company Cytovale (https://cytovale.com/) is developing microfluidic technologies for rapid (less than 10 min) sepsis diagnostics. Despite recent progress, there are several open questions that will be addressed in this PhD project:
•    How are properties of blood cells (e.g. viscoelasticity, size) linked to the observed physical cell behaviour in Cytovale’s microfluidic devices for sepsis diagnostics?
•    How can machine learning and data mining be used to build a predictive model of the cell behaviour?
•    How can the microfluidic device be optimised for maximal diagnostic performance?
Beside the generation of new fundamental knowledge in microfluidics and cell mechanics, the ultimate outcome of the project is a software tool for the optimisation of microfluidic devices that probe mechanical properties of biological cells.
The PhD student will work in an active research group of about 10 modelling researchers (postdocs, PhD students, undergraduate project students) that regularly publish papers in top journals (see www.timm-krueger.de for details).
This PhD project is co-funded by Cytovale. The PhD candidate will regularly interact with Cytovale and be able to visit the company (COVID-related travel restrictions permitting) in the San Francisco Bay Area, California. Cytovale have exhaustive in-house machine-learning and data-mining expertise, as well as multiple testing facilities with regular access to human donor samples and other samples, providing a rich data set of experimental data to confront against numerical modeling results.
The PhD candidate will be trained in scientific research, scientific writing, communication skills, teaching, numerical modeling, coding, machine learning and data mining, efficient project management and career/personal management.


Further Information: 

For more information about Timm Krueger’s research, please go to www.timm-krueger.de.

The University of Edinburgh is committed to equality of opportunity for all its staff and students, and promotes a culture of inclusivity. Please see details here: https://www.ed.ac.uk/equality-diversity

Closing Date: 

Sunday, February 28, 2021

Principal Supervisor: 


An undergraduate degree in Engineering, Physics, Mathematics or Informatics. Moderate coding skills are necessary.

Minimum entry qualification - an Honours degree at 2:1 or above (or International equivalent) in a relevant science or engineering discipline, possibly supported by an MSc Degree. Further information on English language requirements for EU/Overseas applicants.


This is a CASE studentship in collaboration with Cytovale Inc.
Funding is subject to approval by the College or School.

Further information and other funding options.

Informal Enquiries: