POSTDOCTORAL RESEARCHER IN STATISTICAL NLP / MACHINE LEARNING
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Reza E.
May 24, 2012, 6:39:21 AM5/24/12
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POSTDOCTORAL RESEARCHER IN STATISTICAL NLP / MACHINE LEARNING
School of Informatics
University of Edinburgh
Application closing date: 21 June, 2012
The School of Informatics at the University of Edinburgh invites
applications for the post of Postdoctoral Research Associate on the
project "Word Segmentation from Noisy Data with Minimal Supervision",
funded by the EPSRC and supervised by Dr. Sharon Goldwater
(http://homepages.inf.ed.ac.uk/sgwater/).
The project will design and implement novel language models for use in
an unsupervised or minimally supervised setting to simultaneously
perform word segmentation and correction of noise and variability in
the realization of words (as might occur in the output of optical
character recognition, or due to phonetic variability in speech).
Applications of this general approach are many and varied. Examples
include language acquisition (exploring how children begin to identify
words in the speech they hear) as well as OCR and spelling correction.
You will have (or be near completion) a PhD in computational
linguistics, computer science, cognitive science, mathematics, or a
related discipline. You will have proven programming skills and a
background in natural language processing (e.g., language modelling,
finite state methods, corpus processing, evaluation). Experience with
machine learning and probabilistic modelling and inference techniques
is essential; familiarity with issues in cognitive modelling and/or
child language acquisition is also desirable.
The starting date will be 15 Aug 2012, or as soon as possible thereafter.
Fixed Term: 17 months
Salary Scale: £30,122 - £35,938 per annum
Vacancy Reference: 3015763
Closing Date: 21 June 2012
Informal inquiries can be made by email to Sharon Goldwater
(sgw...@inf.ed.ac.uk).
For further details of this position and to submit your application,
please visit http://www.jobs.ed.ac.uk/ and enter the vacancy reference
above, or use the link below.
http://www.jobs.ed.ac.uk/vacancies/index.cfm?fuseaction=vacancies.detail&vacancy_ref=3015763
School of Informatics
University of Edinburgh
Application closing date: 21 June, 2012
The School of Informatics at the University of Edinburgh invites
applications for the post of Postdoctoral Research Associate on the
project "Word Segmentation from Noisy Data with Minimal Supervision",
funded by the EPSRC and supervised by Dr. Sharon Goldwater
(http://homepages.inf.ed.ac.uk/sgwater/).
The project will design and implement novel language models for use in
an unsupervised or minimally supervised setting to simultaneously
perform word segmentation and correction of noise and variability in
the realization of words (as might occur in the output of optical
character recognition, or due to phonetic variability in speech).
Applications of this general approach are many and varied. Examples
include language acquisition (exploring how children begin to identify
words in the speech they hear) as well as OCR and spelling correction.
You will have (or be near completion) a PhD in computational
linguistics, computer science, cognitive science, mathematics, or a
related discipline. You will have proven programming skills and a
background in natural language processing (e.g., language modelling,
finite state methods, corpus processing, evaluation). Experience with
machine learning and probabilistic modelling and inference techniques
is essential; familiarity with issues in cognitive modelling and/or
child language acquisition is also desirable.
The starting date will be 15 Aug 2012, or as soon as possible thereafter.
Fixed Term: 17 months
Salary Scale: £30,122 - £35,938 per annum
Vacancy Reference: 3015763
Closing Date: 21 June 2012
Informal inquiries can be made by email to Sharon Goldwater
(sgw...@inf.ed.ac.uk).
For further details of this position and to submit your application,
please visit http://www.jobs.ed.ac.uk/ and enter the vacancy reference
above, or use the link below.
http://www.jobs.ed.ac.uk/vacancies/index.cfm?fuseaction=vacancies.detail&vacancy_ref=3015763
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