The ADAPT Centre are seeking two talented post-doctoral researchers to join our research team to innovate in the area of Efficient Foundation Models (EFMs). This project aims to develop novel techniques that enable adaptation of FMs to narrow domains and reduce inference costs under resource constraints.

The research will focus on three key areas:

• KT 1: Neural Composition: Investigating modular and compositional architectures to enable flexible domain adaptation by combining the strengths of different modelsKT 2: Adaptive Computation: Exploring techniques such as dynamic pruning / routing, and Mixture-of-Experts models to maximize inference efficiency, without compromising accuracyReinforcement Learning and Optimization: Apply principled machine learning methods to achieve breakthroughs in KT 1 and KT 2

This position offers the opportunity to work at the forefront of AI and NLP research, contributing to high-impact publications, and collaborating with leading academic and industry partners.

• Conduct cutting-edge research on efficient domain adaptation and efficient inference of foundation models (principally language models).

• Develop and systematically evaluate novel approaches for neural composition and adaptive computation, drawing on techniques from multi-objective reinforcement learning and optimization (e.g. evolutionary algorithms).
• Collaborate with interdisciplinary teams, including machine learning researchers and domain experts.
• Publish research findings in top-tier conferences and journals.
• Contribute to the development of open-source tools and frameworks for efficient model training and inference.
• Lead on project documentation and reporting activities