The post of Research Associate (post-doctoral) will conduct research in sign language understanding and generation. The project will leverage cutting-edge research in deep learning, statistical learning theory, and geometric machine learning to create innovative methodologies that are not only high-performing and efficient but also exhibit robustness and reliability in diverse, real-world scenarios. The ultimate goal is to develop a pioneering model for sign language understanding and generation, capable of deployment in practical applications.
The post will be based in the Department of Computing at Imperial College London at the South Kensington Campus. The Department of Computing is a leading department of Computer Science in the UK and the world and has consistently been awarded the highest research rating. In the 2021 REF assessment, the Department claimed the top spot for computer science and informatics. In the latest QS World University Rankings, Imperial College London was recognised as the top university in Europe and the top 2 globally. Overall, Imperial College London ranked first in the UK for research outputs, first in the UK for research environment, and first for research impact among Russell Group universities.
The team at the IBUG group, department of computing, Imperial College is specifically tasked with advancing the understanding and generation of sign language through the development of novel computational models. These models aim to capture the nuances of sign language, including the spatial-temporal aspects and the underlying linguistic structure, to enable accurate recognition and generation of signs. The candidate will undertake research involving both the simulation of sign language gestures and the analysis of real-world data from sign language users. This will involve working with video datasets, motion capture technology, and natural language processing (NLP) techniques. Prior experience in computational linguistics, machine learning, or computer vision is essential, as well as expertise in sign language linguistics or human-computer interaction.
The successful candidate will collaborate with Prof. Stefanos Zafeiriou in the Department of Computing, with members of the IBUG group, and with the rest of collaborators in the project.
In addition to presenting research in top computer vision, machine learning venues, the Research Assistant/Associate will be expected to contribute towards developing, documenting, and maintaining software and modelling tools related to this project.
