The School of Informatics, University of Edinburgh invites applications for two Research Associate positions within the field of machine translation and machine learning.

The Opportunity:
The candidates will work within the Statistical Machine Translation group, which is well known for developing toolkits and running the WMT shared tasks, and has also had a leading role in the field of neural machine translation. It is a supportive group which has many alumni who have moved on to interesting positions in research and industry. We also have access to large GPU clusters and to a regular programme of seminars and workshops with internationally recognised experts.

The researchers will work on one of our EU or EPSRC projects focussed on machine translation. One area of research is making translation significantly more robust in environments with limited training data (GoURMET). The other project (Elitr) is on spoken language translation (SLT), and the aim of this project is to develop systems for multilingual captioning of live audio. The research underpinning this would be in simultaneous SLT, end-to-end SLT or multilingual MT.

The successful candidates will be responsible for a programme which includes fundamental research into deep learning models. They will also be involved in the analysis and deployment of translation systems for our partners such as the BBC resulting in real world impact for our research.

This is an exciting opportunity to work on an internationally visible project, where the interests of the candidates can drive a significant portion of the work.

Your skills and attributes for success:
- A PhD (or near completion) in computer science, artificial
intelligence, or a related discipline,
- Background in machine learning and natural language processing,
- Strong programming skills, ideally including C++.or Python

It would be desirable for you to also have the following:
- Experience in machine translation research,
- Experience with the deep learning toolkits,
- Good publication record.

Please visit our website to apply.

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