PhD Funding in Sustainable Generative AI Models 2026

This project aims to pioneer advancements in the energy-efficient Generative AI models (GenAI), focusing on achieving faster inference times and reduced model sizes without compromising performance and increasing carbon emissions.

As GenAI becomes increasingly central to a wide range of applications, from generating images to generating videos and music, its computational demand and the time required for training and inference have escalated. This research seeks to address these challenges by developing innovative techniques for efficiency, including architectural innovations, compression strategies, algorithmic improvements, and system-level optimizations.

The goal is to enable the deployment of state-of-the-art GenAI models across broader scenarios of computing environments, from high-end servers to consumer-level machines.

This project will contribute to making GenAI more democratic, efficient, and scalable, paving the way for their application in real-time and resource-constrained scenarios. The main research objectives are:

1. to develop cutting-edge techniques for model compression, such as pruning, quantization, and knowledge distillation, tailored for GenAI models
2. to design and experiment with new GenAI architectures that are more efficient, requiring less computational power and memory
3. to create new algorithms and system-wide optimizations to accelerate both training and inference processes for GenAI, making them more suitable for deployment across a variety of computing environments
4. to develop and utilize benchmarks and metrics specifically designed to evaluate the energy-aware efficiency and performance of GenAI under various computational constraints.

You will conduct comprehensive experimental validation and testing. This approach seeks to push the boundaries of what's possible with GenAI, setting new standards for efficiency and accessibility in AI technologies.

Summary:

1. Application Deadline: 27 January 2026
2. Value: Fully Funded (UK and international)
3. Study Level: PhD
4. Sponsor: University of Southampton
5. Course to Study: Artificial Intelligence, Engineering, Physical Sciences
6. Eligible Country: All Countries

Eligibility Criteria for PhD Funding in Sustainable Generative AI Models 2026

You must have a 2:1 honours degree, or its international equivalent.

The AI for Sustainability CDT is multidisciplinary, and we welcome applicants from diverse disciplines, including but not limited to:

1. engineering
2. social science
3. economics
4. business
5. computer science
6. mathematics
7. electronics
8. physical science

You should have an interest in multidisciplinary research and an aptitude for data analytics as well as other skills relevant to one or more of the core themes within the CDT.

Academic attainment is only one of our criteria for selection; we equally value the ability to work in teams, excitement for research, enthusiasm for the research focus of the CDT and the ability to communicate ideas. EU and Overseas applicants should achieve an IELTS score of 6.5 with at least 6.0 in each competency.

Benefits of PhD Funding in Sustainable Generative AI Models 2026

1. Fully funded (UK and international)

How to Apply

Apply now

You need to:

1. choose programme type (Research), 2026/27, Faculty of Engineering and Physical Sciences
2. select Full-time or Part-time
3. search for programme iPhD AI for Sustainability (9220)
4. add the name of the supervisor in section 2 of the application

Applications should include:

1. research proposal
2. your CV (resumé)
3. 2 academic references
4. degree transcripts and certificates to date
5. English language qualification (if applicable)

For more details, visit University of Southampton Scholarship webpage

Application Deadline

January 27, 2026

Application Link(s)

• Apply for PhD Funding in Sustainable Generative AI Models 2026