PhD studentship in Machine Learning and Artificial Intelligence (AI) Systems: Distributed Deep Learning, Personalising Foundation Models and Building Efficient Autonomous Distributed AI

Machine Learning and Artificial Intelligence (AI) Systems

Please contact Amos Storkey (amos+phdapps@inf.ed.ac.uk) as soon as possible about this opportunity.

One fully funded Machine Learning PhD position to work with Prof Amos Storkey in the School of Informatics at the University of Edinburgh, on a project titled “Machine Learning and Artificial Intelligence (AI) Systems: Distributed Deep Learning, Personalising Foundation Models and building Efficient Autonomous Distributed AI”.

This project looks at advancing state of the art in current methods in Machine Learning, especially Deep Learning, Reinforcement Learning and Artificial Intelligence (AI) by making AI methods more efficient, and making learning capability more autonomous and distributed. This leads to integration and communication between different independent machine learning and AI systems.

E.g. When we build AI tools using Machine Learning to e.g. plan for the traffic for an event, it is affected by all the other elements – weather may affect the demand for the event, time of day may affect the interaction with work commute patterns etc. A separate AI tool to predict e.g. the effects of different ticket pricing mechanisms for the event will depend on much the same information – it would be good for the AI tools to integrate. But how can they?

The researcher on this project will spend time pioneering new machine learning, AI and deep learning methods to make modern machine learning models, foundation models and generative AI techniques more efficient effective and distributed. We want methods to no longer aggregate towards a small number of large players in the market. We want to democratize AI.

Candidate’s profile

• a strong degree or higher qualification in a relevant field (e.g. artificial intelligence, machine learning, computer science, mathematics, engineering, physical sciences, economics or any other field where evidence is provided of sufficient computing and mathematical background)
• solid experience of programming, learning methods and ideally deep learning environments (e.g. pytorch) or a computer systems background
• preferably, good mathematical skills and an understanding of either computer systems architecture or economic systems
• demonstrable writing capability.

Studentship and eligibility

This funded post is suitable for a home student (e.g. students ordinarily resident in Scotland or the rest of the UK – England, Wales or Northern Ireland, Republic of Ireland, and EU-EEA nationals with Pre/Settled status). Overseas students can be considered for competitive funding.

Application information

We advise eligible and potentially interested students to contact amos+phdapps@inf.ed.ac.uk (amos+phdapps@inf.ed.ac.uk), Professor of Machine Learning and Artificial Intelligence (AI) as soon as possible with a CV and statement of research interest for more information, and an informal discussion of the PhD position.

For more information please visit: PhD studentship in “Machine Learning Systems: Towards methods for community-integrated autonomous federated AI agents” The University of Edinburgh

Environment

The School of Informatics is one of the largest in Europe and currently the top Informatics institute in the UK for research power, with 40% of its research outputs considered world-leading (top grade), and almost 50% considered top grade for societal impact. The University of Edinburgh is constantly ranked among the world’s top universities and is a highly international environment with several centres of excellence. It has been researching Artificial Intelligence (AI) for over 60 years, and has pioneered many key machine learning methods.