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.