[School of Engineering PhD Scholarships] A microfluidic and data-driven characterisation platform for engineering scale-up robust microbes

About the project

About the Project How do we design the next generation of sustainable biomanufacturing processes? Why do microbial strains that perform brilliantly in the laboratory often fail when scaled to industrial production? This PhD project sits at the intersection of biotechnology, chemical engineering, data science and microfluidics. You will develop an innovative experimental and computational platform to understand how microbial cells behave in the complex environments. Cell factories are increasingly used to manufacture fuels, speciality chemicals, materials and pharmaceuticals that can replace unsustainable petrochemical processes. However, the major challenge that prevents many promising technologies from reaching commercial deployment is Scale Up. Cells that are engineered and optimised under highly controlled laboratory conditions show dramatic drops in productivity when transferred to industrial bioreactors. Large-scale fermenters expose cells to continuously changing conditions, including fluctuations in oxygen availability, nutrient concentrations, pH and mechanical stress, leading to cellular stress, reduced performance and costly process failures. To address this challenge, you will develop a novel micro-scale bioreactor platform capable of recreating the environmental heterogeneity experienced in industrial fermentations. Using microfabrication and microfluidics, you will design systems that generate controlled chemical and physical gradients while enabling continuous observation of microbial populations at single-cell resolution. Advanced fluorescence microscopy will be used to measure growth, physiological stress and cellular resource allocation in real time. Alongside the experimental work, you will build predictive computational models that connect environmental conditions with microbial physiology and production performance. These models will combine mechanistic descriptions of cellular processes with modern machine-learning approaches, allowing predictions to be made across environments that have never been experimentally tested. The resulting framework will provide new insights into how microbes respond to industrial-scale heterogeneity and identify design principles for more robust strains and processes. Using your platform we will redesign and evaluate currently challenging microbial production systems using an iterative design-build-test-learn approach. The most promising new strains and process strategies will be evaluated in bioreactor systems for a validation at laboratory scale. This project is expected to start in September 2027. Before you apply: We strongly recommend that you contact the supervisors for this project before you apply. How to apply: To be considered for this project you must complete a formal application through our online application portal. If you already have an applicant account this link will directly open an application for PhD School of Engineering Scholarships . If you don’t already have an applicant account, please follow the instructions here. . When applying, please specify the full title and supervisor/s of the project, details of your previous study, and names and contact details of two referees. You must also upload a Supporting Statement describing the motivation to apply to the project, your CV and transcripts of awarded and in-progress university qualifications . Please note late or incomplete applications will not be considered. Equality, diversity and inclusion are fundamental to the success of The University of Manchester and central to all our activities. A diverse research community strengthens creativity, productivity and quality, while increasing the societal and economic impact of our work. We welcome applicants from all career paths, backgrounds and sections of the community, regardless of age, disability, ethnicity, gender, gender expression, sexual orientation or transgender status. We welcome applications from candidates returning to study after a career break or experience in other roles. Flexible study arrangements may be available, including part-time study at 50%, 60% or 80%, subject to the requirements of the project and funder. Eligibility :The standard academic entry requirement for this PhD is an upper second-class (2:1) honours degree in a discipline directly relevant to the PhD in chemical engineering, biological engineering, systems engineering, mechanical engineering, physics, applied mathematics, biotechnology, computer science or related disciplines(or international equivalent) OR any upper-second class (2:1) honours degree and a Master’s degree at merit in a discipline directly relevant to the PhD in chemical engineering, biological engineering, systems engineering, mechanical engineering, physics, applied mathematics, biotechnology, computer science or related disciplines (or international equivalent). Experience in programming, modelling, biological sciences, data analysis or experimental engineering is advantageous but not essential. This project will remain open until filled. If your application is submitted by 1 st November 2026, you can expect a decision by 18 th December 2026. If your application is submitted by 15 th January 2027, you can expect a decision by 30 th March 2027. Self or externally funded students can also be considered for this project. FSESoE

Research areas