A funded PhD position (4 years) in AI is available at the Department of
Applied Mathematics, Computer Science, and Statistics of Ghent
University. The ideal candidate has recently completed or will soon
complete a master in computer science or a similar discipline.
Please encourage potential candidates to apply by Aug 1!
***
Job description
A long standing goal of artificial intelligence is to design systems that
can learn and acquire knowledge by interacting with their environment. A
common and effective way for humans to acquire knowledge is through
consulting readily available textual sources, such as books, newspapers,
web pages and so on. Processing and learning from this type of data
requires the abilities to automatically extract information from text, to
learn rules that fuse together different pieces of information to make
novel inferences and to reason about which extracted and inferred facts
are true.
The offered PhD position is part of a larger project in cooperation with
the KU Leuven, aimed at developing a machine reading system in which the
algorithms for extracting, learning and inference are tightly integrated.
In this project you will work together with other researchers. The focus
of your research will be on the inference part, which is crucial to deal
with inconsistencies that might arise when different sources (possibly
with varying levels of trustworthiness) contradict each other, or due to
errors in the interpretation of the sources (e.g., because they contain
vague or ambiguous language).
One avenue to explore is the use of a stratified possibilistic knowledge
base where formulas and facts are organized into equivalence classes and
ranked from most to least certain. This may confer several advantages in
the context of the project. For example, it can alleviate the problem of
calibrating probability estimates from different algorithms used for
extraction of facts, as we no longer would need to worry about minor
discrepancies in probability estimates. Furthermore, it will allow to
reduce many inference tasks to satisfiability problems. Therefore, it will
be possible to leverage the recent advances in satifiability solvers and
avoid some of the computation burdens of more traditional probabilistic
inference.
All developed techniques and algorithms will be evaluated on information
extracted from text found in biological and medical web pages.
***
Job profile
The position is open to a highly motivated candidate interested to do
research in an academic environment for a period of 4 years in view of a
PhD degree. The ideal candidate will recently have completed or will soon
complete a master in computer science or a similar discipline. He or she
has a large interest in knowledge representation and reasoning, and is a
responsible, communicative and flexible person who is able to plan and
carry out tasks in an independent way. Excellent (honors-level) results in
prior studies are required. The candidate is fluent in spoken and written
English.
***
How to apply
Interested candidates are asked to submit their CV and a motivation letter
to prof. Martine De Cock (martine.dec...@ugent.be).
Screening of credentials will begin Aug 1, 2013 and continue until the
position is filled.
https://www.ugent.be/en/news/vacancies/scientific/tboap30092013
=========================================================
Prof. dr. Martine De Cock
Department of Applied Mathematics,
Computer Science and Statistics
Ghent University
Krijgslaan 281 (S9), tel. : +32 (0)9/264.47.70
B-9000 Gent (Belgium) fax. : +32 (0)9/264.49.95
http://www.cwi.UGent.be
_______________________________________________
uai mailing list
uai@ENGR.ORST.EDU
https://secure.engr.oregonstate.edu/mailman/listinfo/uai
