Project Overview: This PhD project, based at the University of Southampton's School of Ocean and Earth Sciences, investigates how Atlantic inflows shape the hydrography and variability of the North Sea. Using state-of-the-art autonomous ocean glider data collected for the Met Office operational forecasting model, the research will focus on key currents, seasonal variability, and the drivers of marine heatwaves. The project is highly interdisciplinary, combining observational data with advanced simulations to enhance understanding of North Sea variability in the context of climate change. Research Focus: The North Sea's ocean circulation is of significant societal importance and is influenced by Atlantic water intrusions via the Fair Isle Current (FIC) and the East Shetland Atlantic Inflow (ESAI). These currents impact regional hydrography, biogeochemical cycles, and ecosystem dynamics, but their fine-scale structure and variability are not well understood due to historical data gaps. This project leverages high-resolution data from the Met Office Glider (MOGli) project, which has operated continuously since 2023, providing unprecedented coverage of shelf-edge exchange and Atlantic water transport, including during marine heatwaves. Objectives and Methods: The PhD will identify and characterize the FIC and ESAI using temperature-salinity signatures, assess seasonal variability, and analyze the evolution of the meridional front. A novel aspect is the integration of glider observations with regional ocean model output (AMM15), enabling detailed analysis of stratification cycles, heat budgets, and lateral processes linked to heatwave preconditioning. Lagrangian diagnostics (particle tracking models) will estimate inflow and residence times of Atlantic Water. The project will develop skills in handling large observational datasets, numerical modeling, and data synthesis—skills highly valued in environmental science, data analysis, and policy sectors. Training and Development: The IGNITE programme offers comprehensive personal and professional development, including training in MATLAB and Python, glider data processing, regional ocean model analysis, and Lagrangian diagnostics. Students will have opportunities to participate in glider deployments and recoveries, gain experience in Slocum glider piloting, and present research at national and international conferences. Funding: The IGNITE Doctoral Landscape Award provides 3.5 years of funding, including a tax-free stipend at the UKRI standard rate (£20,780 for 2025/26), full tuition (Home and International), and a Research Training Support Grant (£2200/year). The difference between Home and International tuition fees is waived by the University. Eligibility: Applicants should have a UK bachelor’s degree with upper second-class honours or higher in a relevant subject, or an international equivalent. English language proficiency (IELTS 6.5 overall, minimum 6.0 in all components, or equivalent) is required. Experience in programming, data analysis, or environmental sciences is advantageous. Both UK and international students are eligible, but international students should note the competitive quota for funded places. Application Process: Applications must be submitted online by 11:59pm on 8 January 2026. Prospective applicants are encouraged to contact the lead supervisor to discuss suitability before applying. Applicants may apply for up to two projects but are advised to tailor their application to one. The IGNITE programme supports diversity and offers a guaranteed interview scheme for qualifying UK applicants from racially minoritised backgrounds. References: Key literature includes Sheehan et al. (2020), Marsh et al. (2017), and Clark et al. (2022), providing context on North Sea inflows and oceanographic variability. For more information and to apply, visit the project page or contact nerc-dla@soton.ac.uk .

Project Overview: This PhD project at the University of Southampton's School of Ocean and Earth Sciences investigates the causes and impacts of the recent and dramatic decline in Antarctic sea-ice, with a particular focus on the record loss observed in 2023. The project aims to address major unresolved questions regarding the drivers of sea-ice reduction and its far-reaching consequences for the ocean and atmosphere, including potential critical climate transitions. Research Areas: The research will span climate science, marine sciences, meteorology, marine geography, data analysis, mathematical modelling, computational physics, and environmental physics. Students will have the opportunity to develop and apply advanced quantitative and computational skills to analyze large datasets and model complex climate and oceanic systems. Supervision and Environment: The project is supervised by a multidisciplinary team: Prof S Josey, Dr H Ayres, Prof R. M. Marsh, Dr A Meijers, and Dr S Tajouri. The University of Southampton is renowned for its research in ocean and earth sciences, providing access to state-of-the-art facilities and a vibrant academic community. Eligibility: Applicants should have or expect to obtain at least a UK 2:1 honours degree (or international equivalent) in a relevant field such as Earth Science, Environmental Science, Physics, Mathematics, Marine Science, or Meteorology. Experience in data analysis, mathematical or computational modelling, and a strong interest in climate science are highly desirable. Non-native English speakers must meet the university's language requirements. Funding: Funding details are not specified in the current advertisement. Prospective applicants are encouraged to check the project link or contact the supervisors for the latest information on funding availability. Application Process: Applications should be submitted online via the University of Southampton's postgraduate application portal by 8 January 2026. Prepare your CV, academic transcripts, and a personal statement outlining your research interests and suitability for the project. Informal inquiries to the supervisors are welcome. For more information and to apply, visit the project webpage .