Wednesday 26 September 2012

Doctoral Scholarship Award Scholarship in Analytical Models for Learning Processes in FLOSS Communities at United Nation University, Macau - China

Description
This scholarship award is offered by The United Nations University – International Institute for Software Technology (UNU-IIST) in Macau China which is specially offered for PhD candidate to carry out research as part of a project on Peer-Production Approaches to e-Learning within a collaboration between UNU-IIST, the University of Macau and the University of Pisa, Italy. The PhD candidate will be directly involved in the analysis of collaborative networks and Free/Libre Open Source Software (FLOSS) projects, with a special focus on the identification and description of learning processes, development of e-learning models based on the peer-production paradigm, definition of pedagogical metrics for such models and continuous monitoring of their implementation as well as benchmarking towards the outcomes of the preliminary data analysis.

The PhD candidate will be enrolled in the double degree PhD programme in ICT for Sustainable Development of the United Nations University and the University of Pisa, will work under the direction and supervision of Dr. Antonio Cerone, UNU-IIST Research Fellow, and will be also co-supervised by Dr Simon Fong, University of Macau, and by one of the leading members of the Knowledge Discovery and Delivery Laboratory – KDD Lab, a joint research lab of the University of Pisa and ISTI-CNR.

Benefit: The successful applicant will recieve a monthly stipend will be USD 1,000 during the stay in Macau and USD 1,500 during the stay in Pisa. Health insurance is provided free of charge. During the stay in Macau accommodation (except utility costs) is also provided free of charge.

Eligibility
  • Candidates for the position should have a Master degree in computer science, software engineering, or information and communication technology, or in a similar discipline;
  • background and experience in information retrieval, data mining and text mining techniques, machine learning techniques and ontology engineering; strong programming ability;
  • good English writing ability; ability to work both independently and in a team.
  • Previous experience in Free/Libre Open Source Software (FLOSS) project participation and in empirical software engineering, knowledge of antipattern technology, pedagogical metrics and standardised models for learning behaviours and background in statistics are advantages.

Application Procedure
Please visit the link below to download the scholarship application form and the completed application form to: scholarship-ste@iist.unu.edu.

Submission Deadline
15 November 2012

Website Link
http://iist.unu.edu/announcement/phd-scholarship-analytical-models-learning-processes-floss-communities-0

No comments:

Post a Comment