The University of Southampton, one of the United Kingdom’s leading research-intensive universities, is currently inviting applications for four fully funded PhD positions across several prestigious Centres for Doctoral Training (CDTs). These doctoral opportunities are supervised by Dr Hongyu Zhang, alongside internationally recognised academic and industry collaborators.
These PhD programmes are designed for highly motivated graduates with strong quantitative, computational, and analytical backgrounds who are interested in addressing real-world challenges in energy systems, climate modelling, machine learning, optimisation, quantum computing, sustainability, and maritime security.
All positions offer full financial support and are open to both domestic and international applicants, subject to individual programme deadlines.
About the Funding
Each PhD position is fully funded, meaning successful candidates will receive:
- Full tuition fee coverage for the duration of the PhD
- A competitive annual stipend to cover living expenses
- Access to structured doctoral training through UKRI and EPSRC-funded CDTs
- Supervision by internationally recognised researchers
- Opportunities for industry placements, international research visits, and collaborations
- Access to state-of-the-art computational facilities and interdisciplinary research environments
Graduates of these programmes are well-positioned for careers in academia, industry, government, and policy-making.
Available PhD Positions
1. Techno-Economic Modelling of Sustainable Data Centre Integration in the European Energy System
Funding Body:
UKRI AI Centre for Doctoral Training in AI for Sustainability (SustAI CDT)
Application Deadline:
đź“… 27 January 2026
This PhD project investigates how large-scale data centres—which are rapidly increasing in number and energy demand—can be sustainably integrated into the European energy system. The research focuses on balancing energy demand, emissions reduction, and system flexibility while supporting Europe’s net-zero transition.
Using advanced optimisation and energy system modelling tools, the project will explore:
- Long-term European energy system planning
- Energy demand and flexibility services from data centres
- Integration of renewable energy sources
- Cooling technologies and waste heat recovery
- Techno-economic and policy analysis for sustainable infrastructure
This position is ideal for candidates interested in energy modelling, sustainability policy, and AI-enabled optimisation.
2. Quantum Computing for Large-Scale Stochastic Optimisation in Energy System Planning
Funding Body:
EPSRC Centre for Doctoral Training in Quantum Technology Engineering
Application Deadlines:
- UK applicants: 31 July 2026
- International applicants: 31 March 2026
This innovative PhD explores how quantum computing and hybrid quantum–classical algorithms can be applied to solve large-scale stochastic optimisation problems in energy system planning.
The research addresses uncertainty in renewable generation, demand forecasting, and long-term infrastructure investment, contributing to Europe’s decarbonisation and net-zero goals.
Key features of this project include:
- Development of quantum and hybrid optimisation algorithms
- Stochastic modelling of real-world energy systems
- Collaboration with Purdue University (USA)
- Opportunities for industry placements and international research stays
- Engagement with cutting-edge quantum technologies
This position suits candidates with interests in quantum computing, optimisation, and energy systems.
3. Machine Learning and Optimisation for Climate Modelling and Energy System Integration
Funding Body:
EPSRC Centre for Doctoral Training in Mathematics for Our Future Climate (MFC CDT)
Application Deadlines:
- Second round: 11 January 2026
- Final round: 8 March 2026
This PhD focuses on developing machine learning and optimisation frameworks to better integrate climate models with energy system planning. The project aims to improve how climate uncertainty, renewable variability, and long-term climate risks are represented in decision-making models.
Research areas include:
- Climate-driven uncertainty in energy infrastructure planning
- Machine learning for climate and energy scenario generation
- GPU-enabled scalable optimisation methods
- Resilient and low-carbon energy system design
This opportunity is ideal for candidates interested in the intersection of climate science, applied mathematics, AI, and sustainability.
4. Machine Learning and Stochastic Optimisation for Maritime Anomaly Behaviour Identification
Funding Body:
EPSRC and MOD Centre for Doctoral Training in Complex Integrated Systems for Defence and Security (CISDnS)
Application Deadline:
đź“… 20 April 2026
This PhD applies machine learning and stochastic optimisation techniques to detect anomalous maritime behaviour using large-scale Automatic Identification System (AIS) data.
The research supports maritime security, sustainable shipping, environmental protection, and global trade monitoring. It combines advanced data analytics with real-world validation through collaboration with maritime stakeholders.
Core research themes include:
- Anomaly detection in complex and uncertain datasets
- Hybrid machine learning–optimisation approaches
- Real-world testing and validation
- Applications in defence, security, and sustainability
This position is well-suited to candidates with strong interests in data science, optimisation, and security applications.
Eligibility and Required Skills
Applicants should have a strong academic background in one or more of the following areas:
- Applied mathematics, optimisation, or operations research
- Energy systems, climate modelling, or sustainability
- Machine learning, artificial intelligence, or data science
- Programming experience (e.g., Python, Julia, GPU programming, or similar tools)
A strong interest in interdisciplinary research and problem-solving is essential.
How to Apply
- Visit the specific application webpage for your chosen PhD position (links provided on the official listing).
- Prepare the required documents, which typically include:
- Academic transcripts
- Curriculum Vitae (CV)
- Personal statement or research motivation
- References
- Submit your application before the stated deadline.
- Shortlisted candidates may be invited for interviews or further assessment.
Applicants are encouraged to apply early due to competitive selection processes.
Contact Information
For questions or further clarification, prospective applicants may contact:
Dr Hongyu Zhang
📧 Email: Hongyu.Zhang@soton.ac.uk
Additional detailed information sheets for each project are available via the official application pages.
These fully funded PhD positions at the University of Southampton represent an outstanding opportunity to pursue doctoral research at the forefront of energy, climate, quantum computing, machine learning, and security studies. With comprehensive funding, expert supervision, and global collaboration opportunities, successful candidates will be well-equipped to shape the future of sustainable and resilient systems worldwide.
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