🎓 Applied Mathematics PhDs

Explore how to develop mathematically rigorous methods ensuring safety and reliability in autonomous systems by integrating control theory, formal verification, and probabilistic approaches. Ideal for candidates eager t…

This research is crucial for advancing the safety and reliability of autonomous systems deployed in real-world safety-critical applications…

Explore multiscale blood flow and cell mechanics through computational and machine learning models. Integrate experimental data with simulations to advance biomedical applications in blood diseases.

This research addresses critical challenges in understanding blood flow mechanics and disease pathology through integrated computational an…

Explore power grid resilience and energy security by developing autonomous grid segments and applying AI techniques. Enhance sustainable energy systems capable of withstanding extreme disruptions. Collaborate internatio…

This research tackles critical vulnerabilities in modern power grids exposed by increasing reliance on renewable energy and more frequent e…

Explore particle-based simulations to understand wet and cohesive powders central to multiple industries. Develop efficient reduced-order models and optimization approaches to improve predictions and mixing quality in r…

This research addresses critical challenges in predicting and controlling the behavior of wet and cohesive powders, which impact pharmaceut…

Explore numerical methods that exploit near-resonance theory to accelerate nonlinear oscillatory PDE simulations in parallel computing. Develop and test algorithms that improve speed and accuracy for applications in flu…

This research advances computational techniques essential for climate prediction, fluid simulation, and geophysical modelling by enabling a…

Explore blood-borne biomarkers for early lung cancer detection using matched proteomic, miRNA, and metabolomic data. Integrate multi-omics with novel network and survival models to uncover early disease signals.

This research has the potential to revolutionize lung cancer detection by identifying biomarkers detectable years before clinical diagnosis…

Explore the dynamics of invasive insect pests transported by wind to Great Britain. Develop and apply quantitative models combining ecology and atmospheric sciences to predict pest spread and strengthen forest biosecuri…

This research addresses critical biosecurity challenges by quantifying the role of wind in dispersing invasive tree pests, a pathway often …

Explore advanced compression methods for massive XCT data to reduce storage needs by up to 80% without losing vital scientific detail. Develop predictive models exploiting XCT data redundancy within an open-source frame…

This project addresses the critical challenge of managing enormous volumes of XCT data, which currently pose storage, transmission, and arc…

Explore advanced numerical methods that leverage near-resonance theory to simulate complex nonlinear oscillatory PDEs efficiently. Develop parallel-in-time algorithms optimized for scalability and energy efficiency in h…

This research enhances computational approaches for simulating critical oscillatory PDEs used in fluid dynamics, weather prediction, and cl…

Develop parallel numerical methods informed by near-resonance theory to accelerate nonlinear oscillatory PDE models. Explore how fast oscillations influence long-term dynamics and optimize algorithms for high-performanc…

This research advances understanding and simulation capability of nonlinear oscillatory PDEs critical for fluid dynamics, weather, and clim…

Funded PhD at the University of Bristol developing modelling frameworks to understand connectivity and dispersal in marine ecosystems under changing environmental conditions.

This PhD quantifies single-turbine wake turbulence to improve understanding of flow physics, aerodynamic interactions, and impacts on downwind turbines, combining numerical simulation, data analysis, and model developme…