Organ-on-chip (OoC) technologies have become increasingly important in the study of disease aetiology and the development of drugs to intervene in disease processes. These miniaturized flow devices sustain cell cultures, often with multiple cell types and in 3-dimensions over extended periods in order to model tissue structure and behaviour in the body more accurately. However, they regularly present challenges to the researcher, not least the paucity of tools for multiparametric, real-time measurement of cell behaviour and response to stimuli during an OoC experiment.A significant opportunity exists in the application of artificial intelligence and machine learning approaches to harness the true experimental power of OoC platforms. But a gap needs bridged between the data-intensive requirements of AI approaches and the data-sparse outputs of existing OoC technologies. OoC platforms needs instrumented to generate the massive datasets needed to power AI.This PhD will explore the available technologies to enable high-content analysis of OoC systems. Three discrete, yet multiplexable, approaches to achieve this will be considered in parallel: Engineering of the cells to enable reporting of phenotype (e.g., Tx/Tl); Decoration of the chip with sensor technologies for in situ monitoring (e.g., pH, pO, temperature, cell impedance, biomarkers); and, stand-off, trans-chip measurement (e.g., microscopy & spectroscopy).This cross-institutional collaboration benefits from the expertise and joint infrastructure of the University of Edinburgh and Heriot-Watt University, creating a world-class environment to pursue a project which promises great impact and reach in engineering for healthcare.For further information please contact Prof. Alistair Elfick (University of Edinburgh) or Dr Sally Peyman (Heriot-Watt University). Further information 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:  Fri, 28/02/2025 - 12:00 Apply now Principal Supervisor Professor Alistair Elfick Assistant Supervisor Dr Sally Peyman (Heriot-Watt) Eligibility 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. Funding Applications are welcomed from self-funded students, or students who are applying for scholarships from the University of Edinburgh* or elsewhere*A number of scholarships are available through a competitive process. Applicants are encouraged to contact the project’s supervisor, Prof Elfick, to discuss their interest in applying for the project, and scholarships.Further information and other funding options. Informal Enquiries Alistair.Elfick@ed.ac.u