PhD in AI-driven prediction of environmental stress sensitivity across pollinators Queen Mary University of London in United Kingdom

PhD opportunity at Queen Mary University of London in the Parkinson Lab, School of Biological and Behavioural Sciences, focused on AI, genomics, ecology, environmental toxicology, and pollinator risk.

The project is titled AI-driven prediction of environmental stress sensitivity across insect pollinators and aims to build predictive, data-driven models that estimate how bee species respond to environmental stressors such as pesticides and combinations of multiple stressors. The research combines genomic features, species traits, toxicity data from the literature, and ecological information to forecast sensitivity across hundreds of bee species, including unseen species and novel stress scenarios.

The student will work in an interdisciplinary environment with training in machine learning, Bayesian modelling, probabilistic inference, large-scale data integration, and AI pipelines for literature mining. The project includes collaboration with experimental researchers using high-throughput sublethal toxicity testing, linking computational prediction with empirical validation. Co-supervision is provided by Chris Bass at the University of Exeter and Chema Martin-Duran at Queen Mary University of London.

Eligibility: candidates should have or be expecting a first or upper-second class honours degree and a Master’s degree in a relevant field such as bioinformatics, computer science, computational biology, statistics, biology, or a related quantitative discipline. Strong programming skills, preferably in Python, and experience with large datasets are expected. Prior research experience is required; machine learning, genomic data analysis, Bayesian methods, large language models, or sequence analysis are beneficial but not mandatory. International applicants must meet English language requirements.

Funding: this is a 3-year funded studentship covering home tuition fees plus an annual tax-free maintenance stipend of £22,618 in 2026/27. International students need external funding for the difference between home and overseas fees.

Application deadline: 2026-06-05. Apply online and submit a CV, personal statement, two academic references, and transcripts/degree certificates.