A fully funded PhD position is available at the University of Leeds through the BBSRC Yorkshire Bioscience Doctoral Training Partnership (YBDTP). The project focuses on decoding how cells decide which mRNAs to translate, leveraging Direct-FRAC-Seq, long-read RNA sequencing, and advanced AI-enhanced graphical models (GmGM). The research sits at the intersection of statistics, AI, computational biology, bioinformatics, and RNA biology, offering a unique cross-disciplinary training environment. The supervisory team includes Dr Luisa Cutillo, Dr Julie Aspden, Prof David Westhead (all University of Leeds), and Prof Claudio Angione (Teesside University), providing expertise across mathematics, machine learning, bioinformatics, and experimental RNA biology. The studentship is fully funded for four years, covering tuition and a UKRI tax-free stipend, and is open to UK, EU, and international applicants (note: international students must cover their own visa, NHS surcharge, and travel costs). Applicants should have a strong background in mathematics, statistics, computer science, bioinformatics, or a related field, and an interest in computational or RNA biology. The application deadline is 5pm (UK), 7 January 2026. To apply, submit the Expression of Interest form online. For questions about the application process, contact [email protected]; for project-specific queries, contact Dr Luisa Cutillo at [email protected].

This fully funded PhD opportunity at the University of Leeds (School of Mathematics) focuses on developing advanced machine learning and statistical techniques to quantify uncertainty in climate impact assessments on biological systems. Supervised by Associate Professor Luisa Cutillo and Professor David Westhead, the project leverages gene expression data from diverse organisms to model biological responses to climate stressors such as heatwaves and changing precipitation. Students will work with high-throughput datasets (e.g., RNA-seq) and apply probabilistic models, including Gaussian graphical models and Bayesian frameworks, to map gene and protein network changes under climate stress. The research aims to identify pathways of resilience or vulnerability in plants, animals, and marine life, supporting risk assessment and adaptive strategies for climate change. The project is ideal for candidates with strong backgrounds in mathematics, statistics, or computer science, and an interest in environmental or biological applications. Experience in statistical modeling, machine learning, or data science is preferred, and familiarity with probabilistic methods or network analysis is advantageous but not required. The UNRISK Centre for Doctoral Training provides full funding, including stipend and tuition, and offers collaborative opportunities within its interdisciplinary network. Applications open on 1st October 2025 for a 2026 start. For more information and to apply, visit the project link and the UNRISK CDT website.