University of Southampton

Southampton is a research-intensive university and a founding member of the Russell Group, and home to 25,000 students and 6,000 staff.

The university ranked 8th in the UK for research intensity and 81st worldwide (QS, 2024), with continued substantial growth in research funding and 7th in the UK for the total value of The Engineering and Physical Sciences Research Council (EPSRC) grants. The university is also a founding partner of SETsquared, the university business incubator ranked 1st in the world (UBI Global).

Research is divided into four broad themes: Agriculture, Food and Health; Environment; Heritage and Creativity; and Prosperity and Resilience.

Ocean & Earth Science ranked 38th and Statistics & Operational Research ranked 48th in the world (QS, 2024).

The university is a three-times winner of the Queen’s Anniversary Prize,

Southampton holds a Silver Athena Swan award for our commitment to gender equality and continued efforts to support the career aspirations of females.

 Potential PhD projects

1. Mapping oceanographic variables with data-driven and physics-informed statistical methods
   Ocean variables such as temperature, salinity, and nutrients vary across a wide range of spatio-temporal scales. Ocean measurements are often patchy, making estimation of underlying structures in space and time challenging. In this project, we aim to develop novel and improved statistical methods for the spatio-temporal mapping of these properties.
   Partner: National Oceanography Centre(NOC).
2. Decadal changes in the North Atlantic Ocean marine environment unravelled with causal deep learning and dynamic network modelling
   The North Atlantic Ocean is strongly impacted by climate change, and data show ongoing changes in its nutrient distributions and marine ecosystem. This project aims to identify the drivers of these changes by applying deep learning techniques for causal attribution and network analysis to a large variety of oceanographic data.
   Partner: National Oceanography Centre(NOC).
3. Using Machine Learning emulators to accelerate Bayesian analysis in Climate Models
   Numerical models of the ocean and climate are complex – calibrating them is hard!GPU-accelerated Machine Learning technologies bring new capabilities to learn thebehaviour of numerical models and aid with calibration. Bayesian methods accelerated by Machine Learning emulators will enable an uncertainty quantification of ocean simulations and climate projections.
   Partner: National Oceanography Centre (NOC).
4. Probabilistic Representation Discovery for Long-Tailed Visual Recognition
   In this project you will develop probabilistic, label-efficient methods to discover structures in long-tailed environmental image data. You will explore how / your initial approach will leverage the learning of prototypes and hierarchies from unlabeled data, improves recognition of rare phenomena, and supports open-world detection across ecology and ocean observation for biodiversity monitoring.Partner: National Oceanography Centre (NOC).
5. Understanding Glacial Cycles Using Machine Learning Emulators of Earth System Models coupled with Ice-Sheet Models
   Why do ice ages recur every 100,000 years? This project combines climate modelling with machine learning to efficiently simulate climate over long timescales, helping us uncover the driver of glacial cycles. You will gain expertise in palaeoclimate, statistics, and machine learning: skills that will help us better understand future climate change.
6. Sea-ice characterisation from autonomous underwater vehicles
   This PhD will develop innovative acoustic methods to characterise Arctic sea-ice, critical for climate, ecosystems, and safe navigation. The project combines polar science, acoustic data analysis, mathematical theory and modelling, providing unique training at the intersection of mathematics, oceanography and engineering in real-world ArcticPolar research.
   Partner: National Oceanography Centre (NOC) & Defence Science and Technology Laboratory (Dstl) & Imperial College London
7. Tracing Cloud Formation Pathways Across Earth’s Atmospheric History
   This PhD project investigates cloud feedback and atmospheric composition in Earth’s climate history, focusing on the Archean aeon. Using atmospheric modelling, palaeoclimate analysis, and cloud physics, the student will develop new tools for 3D climate models, advancing understanding of climate change, atmospheric dynamics, and the role of clouds.
8. Can individual environmental responses predict associations between speciation, extinction and climate change?
   You will build integral projection models for species with complex demography, using stochastic processes to explore the evolutionary stable strategies that lead to species’ emergence and persistence. Your models will be compared against state-of-the-art integrated geochemical and computer tomographic data to make rigorous inferences of pivotal eco-evolutionary processes.
   
9. Ultrasonic gas leak localisation and characterisation in complex environments
   This PhD project will develop advanced ultrasonic array techniques for hydrogen leak detection, localisation and characterisation in complex, noisy environments. Combining mathematical modelling and physics-informed signal processing with AI-driven methods (including PINNs), the research aims to enhance robust, cost-effective leak detection with industrial applications on complex sites.
   Partner: Shell
10. Spatio-Temporal Data Fusion Methods for Ocean Dynamics Modelling
    This project aims to integrate in-situ and remotely sensed data to improve climate and environmental modelling. By advancing spatio-temporal statistical methods for data fusion, it addresses challenges in combining diverse datasets and capturing complementary information to enhance understanding of ocean dynamics. Applications include predicting marine abundance and managing environmental challenges.
    Partner: Imperial College London
11. Changepoints in Climate: from Fixing Records to Detecting Tipping Points
    Advance the frontier of climate science by developing novel algorithms for detecting change points, critical for understanding catastrophic climatic shifts (tipping points) and observational discrepancies. This PhD project, will refine climate predictions and help to inform global policy making by leveraging.
    Partner: University of Reading, University of Lancaster, Colorado School of Mines, National Center for Atmospheric Research, USA
12. Cryosphere and Underwater Remote Inspection and Observation using Optic-fibre based Sustainable noise-InTerferometrY (CURIOSITY)
    This project will demonstrate a novel approach combining seafloor cables with natural sound to characterize the ocean and cryosphere at unprecedented scales. There will be two major advances: extension to large data sets from optical fibre sensing (empirical greens functions and kernel methods) and application to ice covered oceans (numerical modelling).
    Partner: NOC
13. Detecting deep water formation in the North Atlantic using remote sensing, machine learning and mathematical methods
    Deep convection has a key role in dense mass formation, and ultimately impacts the Atlantic Meridional Overturning Circulation. This project will investigate these events by developing a machine learning approach to integrate in situ measurements, remote sensing, and high-resolution ocean simulations.
    Partner: NOC
14. Project Title Reconstructing High-Resolution Climate Records: Deconvolution of Marine Sediment Data to Unveil Past Climate Dynamics
    Palaeoclimate records from marine sediments are vital for understanding past climate changes but are often distorted during deposition and measurement processes. This project develops Bayesian and AI-based deconvolution methods, validated through experiments, to restore high-resolution and accurate climate reconstructions to confront and inform climate models for improved future predictions.
    Partner: Geological Survey of Japan, AIST, Japan
15. Machine learning and optimisation for climate modelling and energy system integration
    

    This PhD will develop machine learning and optimisation methods to improve climate modelling and integrate climate models with energy system models. The project will leverage GPU programming and provide decision-support tools for sustainable planning under climate uncertainty, contributing directly to Europe’s energy transition and net-zero targets.