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Active Learning for Language and Multimodal Applications

Primary supervisor

Reza Haffari


Research area

Vision and Language

This PhD project aims to mitigate the data scarcity of new NLP and Multimodal applications by developing novel active learning algorithms. In this project, the student will leverage large foundation models, such as ChatGPT and GPT4, incorporating the cutting-edge techniques in the other areas, such as reinforcement learning, causality and GFlowNets, to devise novel active learning algorithms for NLP and multimodal applications.

Required knowledge

Candidates are expected to have a solid background in machine learning and Natural Language Processing. Research experience in multimodal research is desired. Preference will be given to candidates who have strong written and oral communication skills, as well as strong programming skills. It is desirable that the candidates already have research experience in at least one of the following areas: deep learning, active learning, deep reinforcement learning, and natural language processing.

Project funding


Learn more about minimum entry requirements.