AI Driven Transformation of UK Aeroallergen Monitoring for Public Health Impact

AI Driven Transformation of UK Aeroallergen Monitoring for Public Health Impact 

• UK Health Security Agency (UKHSA) & University of Leicester  
• Start date: Sept–Oct 2026
• Funding: UKHSA Studentship (Home fees + stipend + research/travel costs)

Project Overview:
Hayfever and other respiratory allergies affect millions in the UK and place a major burden on healthcare, education, and productivity. Reliable, real time pollen data is vital for symptom management, clinical care, and forecasting, yet current monitoring depends on slow manual methods that cannot provide real time information. Automated systems like the SwisensPoleno Jupiter can provide rapid, high resolution data using holography, fluorescence, and machine learning classification, but existing models are mostly trained on European datasets and perform poorly for UK specific pollen. With climate change intensifying pollen seasons, the demand for accurate and timely allergen information is greater than ever.
This PhD offers a unique opportunity to apply machine learning and AI to modernise the UK’s aeroallergen monitoring system, delivering real time data that can directly benefit allergy sufferers, clinicians, public health responders, and national forecasting systems.
You will develop a UK specific, AI enabled data processing pipeline for automated bioaerosol sensors, rapidly transforming raw particle imaging data into concentrations of different pollens within the air at any given time.
This is a rare chance to work at the cutting edge of AI for public health, contributing to the foundations of a future automated UK pollen network.

Project Highlights:

• Conduct a review on the public health impacts of pollen allergies and current monitoring methods, building the case for an automated UK pollen network.
• Help generate a high quality UK pollen training dataset through pollen collection, laboratory preparation, and controlled aerosolisation experiments.
• Analyse and compare datasets from different measurement methods and instruments, evaluating model performance across regions and environmental conditions.
• Develop machine learning classification models trained on UK specific pollen datasets.
• Evaluating and improving a real time data pipeline for automated aeroallergen detection.
• Contribute scientific evidence supporting public health alerts and allergy risk forecasting.
• Collaborate with national partners including the Met Office, Environment Agency, HSE, and the UK & Ireland Poleno User Group.
• Undertake training in aerobiology, microscopy, aerosol science, data science, and AI techniques.
• Produce high impact research with direct real world application.

Why This Project Is Exciting:

Advanced machine learning + real time sensing
You will work with cutting edge automated bioaerosol instruments and apply AI to live environmental data — a rapidly growing field with multiple career pathways.
Hands on interdisciplinary research
From collecting pollen samples and analysing holographic particle images to building and evaluating ML models, this project blends experimental and computational science.
National impact
Your findings will help shape the UK’s emerging automated aeroallergen monitoring network.
High public health relevance
Your work will enhance early warning systems for allergy sufferers and help clinicians support patients during high risk periods such as peak pollen days or thunderstorm asthma events.

Training & Support:

You will join an interdisciplinary team spanning environmental health, aerosol science, allergy, data science, and machine learning.
You will receive training in:
• Pollen and fungal spore identification
• Automated aeroallergen monitoring
• Aerosol generation and sampling
• Python based data processing
• Machine learning model development
• Statistical analysis
• Public and patient engagement
• Scientific writing and dissemination
You will spend approximately 75% of your time at UKHSA on the Harwell Science Campus, Oxfordshire and 25% at the University of Leicester, including a dedicated training placement in Year 1.

Supervisory Team:

UKHSA: Dr Alison Buckley (Principal Supervisor), Dr Emma Marczylo, Dr Sophie Mills
University of Leicester: Prof Anna Hansell, Dr Fiona Symon, Prof Huiyu Zhou
Supported by the UK & Ireland Poleno User Group Advisory Board, including the Universities of Leicester, Manchester and Essex, Dublin City University, UKHSA, Met Office, Environment Agency and HSE.

Qualifications:

We invite applications from candidates who hold/or expect to gain a first or upper second-class honours degree (or equivalent), or a relevant Master’s degree in one of the following areas:
• Atmospheric, Biological or Environmental Science (e.g., Atmospheric Science, Meteorology, Environmental Science, Environmental Health, Physical Geography)
• Data or Computational Science (e.g., Data Science, Computer Science, Artificial Intelligence, Statistics, Applied Mathematics, Physics, Engineering)
• Other closely related disciplines (e.g., Biological Sciences, Ecology, Botany) that provide relevant skills in data analysis, statistics, modelling or machine learning.

Essential:

• Strong quantitative or computational skills and/or enthusiasm to develop them
• Interest in applying ML/AI to real world public health challenges
Desirable:
• Experience with Python, machine learning, or statistical modelling
• Experience in environmental monitoring, air quality, or exposure science
• Knowledge of pollen, aeroallergens, or related biological/environmental topics

Funding:

The project is funded by UKHSA, an executive agency of the UK Department of Health & Social Care. The studentship covers: UK home tuition fees (3 years); Annual stipend aligned with Wellcome Trust rates (~£26,000 starting); research consumables & travel funds; public involvement/engagement budget and IT & training support.

Applications & Interviews:
For more information or questions please contact alison.buckley@ukhsa.gov.uk.

To apply please send your CV and a personal statement (1 – 2 pages) explaining your motivation for applying, your relevant skills, experience or academic background and why you are suited to this project to alison.buckley@ukhsa.gov.uk.

The application deadline is 30th April 2026. Please note we may close this opportunity early if we fill the position.
Interviews are expected to be help in April/May with a start date in October. In person interviews are preferred and will be held at UKHSA on the Harwell Science Campus, Oxfordshire, OX11 0RQ.