About the Project
Can a low-cost sensor learn to smell?
Volatile organic compounds (VOCs) released by food and buildings carry valuable information: they reveal when food is spoiling and when mould is growing behind a wall. Laboratory instruments can read these signals, but they are expensive and largely confined to laboratories. Low-cost gas sensors can be deployed in fridges, homes and buildings at scale, yet today each application needs its own bespoke design, and models stop working when sensors age or are replaced.
This PhD will develop the foundations of transferable, deployable machine olfaction. The project treats odour sensing as a communication system, where the sample is the transmitter, air is the channel, and the sensor array is the receiver. This view allows questions such as "how much useful information does this sensor array actually capture?" to be answered with measurable, information-theoretic tools.
You will:
- Build and characterise low-cost gas sensor arrays in a controlled test chamber, using temperature-modulated operation to extract richer response features.
- Create labelled datasets for food spoilage, with laboratory reference measurements, and for indoor damp and mould.
- Develop machine learning methods (contrastive and few-shot learning, domain adaptation) that remain reliable as sensors drift and transfer between devices and applications.
- Use information-theoretic analysis to guide sensor array design, and deploy models on low-power microcontrollers.
The project builds on Waste-Not!, an award-winning in-fridge "artificial nose" already deployed in 30 households and a commercial cold store, and on partnerships with housing providers, local authorities and industry. You will have access to real-world data and deployment sites, and your work will contribute to reducing food waste and creating healthier homes.
The project sits within the Department of Electrical and Electronic Engineering and connects with the University's Digital Futures and Sustainable Futures platforms. You will be supervised by Dr Oktay Cetinkaya (embedded systems and networks, multi-sensor fusion, machine learning and low-power communications) and Prof Alex Casson (low-power wearable sensing and biomedical signal processing). You will receive training in gas sensing and calibration, experimental design, machine learning, information theory and embedded implementation, alongside Doctoral Academy training in research skills and communication. You will be encouraged to publish in leading journals, present at international conferences and engage directly with project partners.
This is an opportunity to work at the intersection of electronics, data science and chemistry on a problem with clear societal impact, at a time of rapidly growing national investment in artificial olfaction.
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 (or international equivalent) in Electrical and Electronic Engineering, Computer Science, Physics, Chemistry, Chemical Engineering or a closely related discipline OR any upper-second class (2:1) honours degree and a Master's degree at merit (or international equivalent) in Electrical and Electronic Engineering, Computer Science, Physics, Chemistry, Chemical Engineering or a closely related discipline. Previous research experience in one or more of the following is advantageous: sensors and instrumentation, gas sensing or analytical chemistry, embedded systems, signal processing, machine is desirable.
This project will remain open until filled.
If your application is submitted by 1st November 2026, you can expect a decision by 18th December 2026.
If your application is submitted by 15th January 2027, you can expect a decision by 30th March 2027.
Self or externally funded students can also be considered for this project.
FSE_SoE
Funding Notes
Funding for this project covers tuition fees, UKRI minimum annual stipend (currently £21,805/annum) and up to a £2.5k/annum research training support grant for the full duration of the 3.5-year programme.