The Institute for Language, Cognition and Computation at the University of Edinburgh (School of Informatics) has an open PhD studentship position in the area of natural language processing and machine learning.
The topic of research is the use of linear algebra and spectral methods for predicting linguistic structure, especially through latent-variable modeling or learning from incomplete data. Latent-variable modeling is a common technique for improving the expressive power of natural language processing models. The values of these latent variables are missing in the data, and therefore need to be predicted. This project seeks to identify key techniques, based on linear algebra and spectral methods, in order to learn these models. (Latent-variable modeling can also be broadly construed here, and refer to unsupervised learning as well.) The applications in NLP can vary from syntax to semantics.
The desired qualifications include an undergraduate background in computer science or mathematics (with the latter including some experience with programming). There is a preference for candidates who have completed a master's in one of these areas.
If interested, please apply through here: http://www.ilcc.inf.ed.ac.uk/study. Deadline for applications is 12/12/2014.
For more information about the application process, please contact Shay Cohen at scohen@inf.ed.ac.uk.
About the ILCC:
The Institute for Language, Cognition and Computation (ILCC) at the School of Informatics in the University of Edinburgh is dedicated to the pursuit of basic and applied research on computational approaches to language, communication, and cognition.
Primary research areas include Spoken Language Processing, Natural Language Processing and Computational Linguistics, Information Extraction, Retrieval and Presentation, Dialogue and Multimodal Interaction, Computational Theories of Human Cognition, and Educational and Assistive Technology.
For more information, see http://www.ilcc.inf.ed.ac.uk/.
The topic of research is the use of linear algebra and spectral methods for predicting linguistic structure, especially through latent-variable modeling or learning from incomplete data. Latent-variable modeling is a common technique for improving the expressive power of natural language processing models. The values of these latent variables are missing in the data, and therefore need to be predicted. This project seeks to identify key techniques, based on linear algebra and spectral methods, in order to learn these models. (Latent-variable modeling can also be broadly construed here, and refer to unsupervised learning as well.) The applications in NLP can vary from syntax to semantics.
The desired qualifications include an undergraduate background in computer science or mathematics (with the latter including some experience with programming). There is a preference for candidates who have completed a master's in one of these areas.
If interested, please apply through here: http://www.ilcc.inf.ed.ac.uk/study. Deadline for applications is 12/12/2014.
For more information about the application process, please contact Shay Cohen at scohen@inf.ed.ac.uk.
About the ILCC:
The Institute for Language, Cognition and Computation (ILCC) at the School of Informatics in the University of Edinburgh is dedicated to the pursuit of basic and applied research on computational approaches to language, communication, and cognition.
Primary research areas include Spoken Language Processing, Natural Language Processing and Computational Linguistics, Information Extraction, Retrieval and Presentation, Dialogue and Multimodal Interaction, Computational Theories of Human Cognition, and Educational and Assistive Technology.
For more information, see http://www.ilcc.inf.ed.ac.uk/.
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Posted by: Tri Kurniawan Wijaya <trikurniawanwijaya@yahoo.com>
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