The Research Associate will be funded by the EPSRC (UK) project `Improving Target Language Fluency in Statistical Machine Translation’. The project is focused on developing techniques to produce highly fluent and robust statistical machine translation systems.
Duration: 12 months, renewable for another 12 months, starting Fall 2015. Deadline: The positions will remain open until filled.
http://www.jobs.cam.ac.uk/job/6913/ Reference: NM06012
Candidates should have a Ph.D. in machine learning, natural language processing, speech recognition, or a related area, with interests in any of the following topics : - Statistical machine translation, syntactic SMT - Neural networks, deep learning - Natural language generation - `Big Data' techniques large-scale text processing and for machine learning (e.g. Hadoop, Spark)
For candidates with an interest in supervision of graduate research, there will be opportunities to set and lead research projects on the Cambridge MPhil in Machine Learning, Speech, and Language Technologies.
Please send your CV to Bill Byrne (bill.byrne at eng.cam.ac.uk) or to AdriÓ de Gispert (ad465 at cam.ac.uk). We are happy to answer any questions related to the position or the project. We will be available to meet at WMT and EMNLP in Lisbon in September.
SMT at Cambridge (http://divf.eng.cam.ac.uk/smt): Cambridge SMT researchers have developed the HiFST/HiPDT translation systems (http://ucam-smt.github.io), leading to the 2012 EAMT Best Paper and EAMT 2010 Best Thesis awards. The team participates in international MT evaluations, such as the NIST and WMT shared tasks, with entries consistently ranked among the top submitted systems. Cambridge SMT researchers also have strong industrial connections, with PhD students and RAs going on to take positions at Google, IBM, SDL, Facebook, Nuance, and other top research labs in the UK and USA.
The SMT research team is part of the Cambridge Speech and Language Technologies Group which also carries out research in speech recognition, speech synthesis, and statistical dialogue systems. The SLT Group also has strong collaborative ties to the Natural Language Processing group at the Cambridge Computer Laboratory (http://www.cl.cam.ac.uk/research/nl/) and to the Cambridge Computational and Biological Learning Group (http://cbl.eng.cam.ac.uk).
For an overview of language research at the University of Cambridge, please see the Cambridge Language Sciences website (www.languagesciences.cam.ac.uk) .
-- Bill Byrne Professor of Information Engineering University of Cambridge http://mi.eng.cam.ac.uk/Main/WJB31