PhD in AI Enabled Prediction of Pollen Season Magnitude
Develop novel machine learning methods to predict seasonal pollen magnitude in partnership with the UK Met Office.
Best for:Best for candidates with a strong background in machine learning, data science, or environmental modeling interested in applied AI for earth systems.
Overview
This PhD position at the University of Birmingham focuses on harnessing a unique UK-wide pollen reanalysis dataset from 2000 to 2025 to develop advanced machine learning methods for predicting seasonal pollen magnitude months in advance. Working directly with the UK Met Office and an interdisciplinary team spanning environmental science, mathematics, and artificial intelligence, the student will investigate environmental drivers of interannual variability. The successful candidate will build interpretable models that improve operational pollen forecasting systems. The role provides outstanding training in environmental modeling, machine learning, and operational forecasting within a vibrant academic environment.
What you'll do
- Develop novel machine learning and artificial intelligence models to predict airborne pollen season magnitude months in advance
- Analyze a comprehensive UK-wide pollen reanalysis dataset covering 2000 to 2025 alongside meteorological records
- Investigate biological memory, antecedent weather conditions, and environmental variables affecting plant pollen production
- Collaborate directly with operational scientists at the UK Met Office to integrate models into the NAME pollen forecasting system
- Publish research findings in peer-reviewed academic journals and present outcomes at international conferences
Requirements
Degree required: BSc or MSc in a quantitative discipline such as Computer Science, Mathematics, Data Science, or Environmental Science
Degree offered: PhD
Language: English; standard university English language proficiency requirements apply
Instruction: English
- Strong programming skills in Python or R for data analysis and machine learning
- Experience with machine learning or statistical modeling frameworks
- Solid foundation in data analysis, statistics, or environmental data science
- Ability to work collaboratively in an interdisciplinary research team
- Meets standard entry requirements for doctoral studies at the University of Birmingham and CENTA
Funding & terms
How to apply
Deadline: 2027-01-06
Interested applicants should review the CENTA studentship guidelines, complete the official application form, and submit all required supporting documents before the deadline.
- CV
- Cover letter
- Academic transcripts
- Reference letters
- CENTA application form
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