An open internship position on a mix of string algorithms, grammatical inference and statistical machine learning

Description

We are looking for a motivated intern for a project involving the use of sequential patterns for the inference of grammars.

The Smallest Grammar Problem is the problem of finding the smallest context-free grammars that generates exactly one given sequence. We plan to generalize this in order to find grammars which generate a set of natural language documents with a strong but hidden structure. This structure will then be converted into additional features (through tree kernels for example) in our analytics pipeline or alternatively as a starting template for existing multilingual authoring tools.

Requirements:

• Research-oriented master student or PhD candidate in computer science
• Knowledge of standard text algorithms and data structures
• Knowledge of formal grammars (a course covering the Hopcroft & Ullman book or equivalent for example)
• Knowledge in statistical machine learning applied to text is a strong plus
  
• Fluency in either C, C++ or Java is a plus

The intern will work closely with researchers in a very international environment, and will be strongly encouraged to produce scientific publications.

Application instructions

Informal inquiries are welcome and can be made at matthias.galle@xrce.xerox.com .

To submit an application, please send your CV and cover letter to both xrce-candidates@xrce.xerox.com and matthias.galle@xrce.xerox.com .

Ideally, you will also include in your CV people we can contact for letters of recommendation.

link: http://www.xrce.xerox.com/About-XRCE/Internships/Grammatical-Inference-with-sequential-motifs

--mg