Title: Visual Analytics for heterogeneous text streams. Location: LIRMM Lab (http://www.lirmm.fr/lirmm_eng/contact-‐us), Montpellier (http://en.wikipedia.org/wiki/Montpellier), France Contact: Dr. Arnaud Sallaberry at LIRMM (http://www.lirmm.fr/~sallaberry/) and Dr. Mathieu Roche at CIRAD (http://www.lirmm.fr/~mroche). Abstract: The heterogeneity of data is an important issue in Big Data area. Data is not only large in volume and produced at a high speed (velocity), but also holds many kinds of input (technical heterogeneity), structures (data model heterogeneity) and meanings (semantic heterogeneity). We would like to recruit a postdoctoral researcher for one year to address this specific issue from a visual analytics approach. Context: A lot of documents (web pages, scientific publications, reports, and so forth) contain much useful information. Mining heterogeneous data, according to their structure and to their content becomes a major issue in data mining area. A key problem consists of sharing these various data and/or information and integrating them in order to discover new knowledge. In addition, microbloggings contain crucial information to take into account in a global system that mines heterogeneous data. For instance, people participating in on-line forums, microblogging or discussing on social networks leave behind them digital traces of information on a variety of topics. The analysis of individual messages and their aggregation represent a considerable challenge for currently existing methods, as user-written texts present a special type of stream setting. In this project we plan to investigate the epidemiology issue of farmed animals in collaboration with UMR Cirad-INRA CMAEE. The aim is to detect weak signals concerning the beginning of epidemics (e.g., African swine fever, foot and mouth disease, bluetongue, avian influenza). Description: Visual exploration of textual data is an active area of research. Most of the methods proposed deal with static texts like discourses, books or, more generally, string data. Most of these methods require well-formatted data and are not adapted to streams and/or heterogeneous data. The candidate will be in charge of discovering efficient text mining techniques for extracting structured data, and designing visual interfaces to interact with these structures and explore the data. He/She will process following the steps of the Munzner’s nested model for visualization design. Qualifications: - A PhD degree in Computer Science on the domain of Information Visualization or Visual Analytics with interest in Text Mining. - An excellent publication record, including papers in high‐impact journals and conference proceedings. - Strong experience in programming languages. - Knowledge of visualization libraries (e.g. D3, GraphViz, ...) is an asset. ‐ Must be proficient in English. Supervision: The project will be formally jointly supervised by Dr. Arnaud Sallaberry at LIRMM (http://www.lirmm.fr/~sallaberry/) and Dr. Mathieu Roche at CIRAD (http://www.lirmm.fr/~mroche). Starting date: September/October 2014 (some flexibility is possible) Conditions of employment: Net salary: ~ 2200 euros / month Interested candidates are requested to send an application by e-‐mail to Dr. Arnaud Sallaberry, arnaud.sallaberry@lirmm.fr and Dr. Mathieu Roche, mathieu.roche@cirad.fr with the subject field: 'LABEX: Post-‐Doc visualization position'. The application should consist of a motivation letter and a curriculum vitae with a list of publications and description of any previous research you have held. Furthermore, names and contact information for three references are required. Applications will be reviewed immediately and the review process will continue until the position is filled. Sincerely, Arnaud. -- Arnaud Sallaberry Associate Professor - Maitre de Conferences LIRMM, Universite Paul Valery Montpellier 3 LIRMM - UMR 5506 - CC 477 161, rue Ada 34095 Montpellier Cedex 5 France Tel. : +33 (0)4 67 41 86 53 E-mail : arnaud.sallaberry@lirmm.fr Home Page : http://www2.lirmm.fr/~sallaberry/