Vacancies

  • (New) Postdoctoral researcher Language in Interaction

    Deadline: Sunday 10 September 2017

    This postdoc position is part of the larger Dutch research consortium 'Language in Interaction', sponsored by a large grant from the Netherlands Organisation for Scientific Research (NWO). The goal is to understand both the universality and the variability of the human language faculty from genes to behaviour. The postdoc will be part of a team working on the question "The nature of the mental lexicon: How to bridge neurobiology and psycholinguistic theory by computational modelling?", and will focus on the problem of how to learn word vector representations that encode the combinatorial properties of words required to account for complex linguistic phenomena.

    The postdoctoral researcher should have a PhD degree (or equivalent) in (computational) linguistics, or another relevant field of study, and a strong background in syntax, formal semantics and/or parsing. Knowledge of methods in distributional semantics and machine learning for Natural Language Processing is desirable, as is previous experience with vector space models of semantic compositionality.

    For more information, see here or at www.uva.nl/en/content/vacancies/2017/07/17-377-postdoctoral-researcher-language-in-interaction.html or contact Dr. Willem Zuidema at , or Dr Raquel Fernández at .
  • PhD candidate in deep learning for natural language processing

    Deadline: Monday 31 July 2017

    Appplications are invited for a PhD position in a joint project between the Institute for Logic, Language and Computation (ILLC) and the Institute of Informatics (IvI). This PhD vacancy will focus primarily on deep learning for natural language processing (affiliated with ILLC, supervised by Dr. Ivan Titov). The PhD candidate will spend much of the time posted at the Institute for Language, Cognition and Computation (ILCC) at the University of Edinburgh, where the project leader holds his main faculty appointment.

    The project will attempt to develope deep learning methods for predictive analysis of complex network data (e.g., social networks, trading networks etc.). In this work we will use very large amounts of real data from a trading network (an industrial partner) but the methods will generalize to other types of networks where heterogeneous data is being exchanged. We will be developing predictive algorithms relying on the flow of transactions in the network. We also seek to cluster the businesses trading over the networks as well as the products that are being traded. As information in these networks mostly comes in a textual form, we will develop methods for inducing predictive semantics representations of texts relying both on the text itself but also on the flow of information in the network.

    Necessary qualifications for candidates include excellent grades, proven research talent, affinity with machine learning, statistics and excellent programming skills. A master’s degree in computer science (preferably with a specialization in artificial intelligence and/or machine learning), applied mathematics or computational linguistics. Strong programming skills are required.


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