PhD Studentship: Lifelong Learning and Shareable Models: How to Reduce the Energy Footprint of AI and Contribute to a Sustainable Decarbonised Future

Current AI models are not designed to reuse and share knowledge. When conditions change, e.g., data distributions, locations, or platforms, retraining needs to occur from scratch. In some cases, like for foundation models or for complex robotics tasks, the process requires very large amount of data and energy. Recent advances towards lifelong learning and sharable models promise to create a new efficient AI landscape in which machine-learned knowledge can be built incrementally and worldwide with optimized energy use. This PhD project aims to advance the latest lifelong learning and sharable AI models to contribute to reduce the energy footprint of AI. An overview of this emerging field can be found in a recent publication from our group on Nature Machine Intelligence https://doi.org/10.1038/s42256-024-00800-2

The student will be part of a growing group of researchers in machine learning and artificial intelligence in the Computer Science Department with collaborators from Loughborough Business School. Learn more about Digital Decarbonisation

Dr. Andrea Soltoggio is a Senior Lecturer and a world-leading scientist in the area of lifelong machine learning. Currently, he is leading a growing research group with the aim to advance the state-of-the-art in lifelong machine learning and its applications to real world scenarios. Candidates are strongly encouraged to contact Dr. A.Soltoggio at a.soltoggio@lboro.ac.uk for further details. The project is in collaboration with the School of Business and Economics with co-supervision by Dr. Vitor Castro, Dr. Rebecca Higginson and Prof. Tom Jackson.

Loughborough University has an applied research culture. In REF 2021, 94% of the work submitted was judged to be top-rated as 'world-leading' or 'internationally excellent'. We are a community based on mutual support and collaboration. Through our Doctoral College there are continual opportunities for building important research skills and networks among your peers and research academics.

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