Connectionists: Doctoral Scholarschips in KD / ML

[Ulf Brefeld]

Doctoral scholarships in knowledge discovery / machine learning

In close co-operation with the German Institute for International
Educational Research and Educational Information (DIPF) in Frankfurt
am Main, a member of the Leibniz Association, the Technical University
of Darmstadt is offering Doctoral Scholarships in knowledge discovery/
machine learning within the newly established PhD program "Knowledge
Discovery in Scientific Publications" [1]. The regular maximum
duration of funding is 36 months.

Scholarships are granted for completing a doctoral thesis in computer
science with a strong focus on machine learning. The research is
applied to the domain of educational research literature. To this end,
DIPF offers excellent opportunities for a close co-operation with
subsequent users. Successful candidates will be granted 1,400 Euros
per month. The program will be located at DIPF in Frankfurt (Main).

The successful candidates are expected to work on the projects
"Personalized Content Acquisition from Heterogeneous Sources"
(Prof. Brefeld), or "Preference-Based Profiling of Scientific
Publications" (Prof. Fürnkranz).

The PhD program brings together the disciplines of "Knowledge
Engineering", "Algorithmics", "Language Technology", "Ubiquitous
Knowledge Processing", "Knowledge Mining and Assessment", and
"Information Management". The concept for supervision strongly relies
on close contacts between postgraduate students and their supervisors,
regular joint meetings, co-supervision by professors and senior
researchers from the above disciplines and a lively exchange in the
research and qualification program. Furthermore, the program strives
to publish research findings at leading scientific conferences and
provide its software freely accessible as open source product.

Excellently qualified graduates from computer science are invited to
apply. Successful candidates are expected to possess very good
programming skills in Java, to work independently, demonstrate their
personal commitment, team and communication skills as well as a
readiness to cooperate with others. Research experience, in particular
in machine learning, is a plus.

Women are expressly invited to submit their application. According to
the pursuant legal requirements, applicants with disabilities will be
preferably treated in the appointment procedure. Candidates from
abroad are encouraged to apply.  The Department of Computer Science at
TU Darmstadt regularly ranks among the top in Germany. Among its
distinguishing features are its research initiative "Knowledge
Discovery on the Web" focusing on powerful language technology
procedures, text mining, machine learning and scalable infrastructures
for assessing and aggregating knowledge. As a scientific institute
belonging to the Leibniz Association, the DIPF targets top-class basic
research as well as innovative scientific services. Education is
addressed as an area with high visibility and significance. The DIPF
is currently establishing a research priority domain for educational
information science, by joining competencies with computer scientists
at TU Darmstadt. In this context, the doctoral program will constitute
a central element.  Please submit your application by February 28,
2013. Applications should include a letter of motivation related to
the research program [1] and its corresponding projects [2] and [3],
CV and details regarding previous scientific work, certifications of
studies and work, including the graduate thesis and possibly
electronic publications.

Applications should be sent to Prof. Dr. Iryna Gurevych and
Prof. Dr. Marc Rittberger, e-mail:
phd-application at ukp.informatik.tu-darmstadt.de<mailto:phd-application at ukp.informatik.tu-darmstadt.de>.

[1] http://www.kdsl.tu-darmstadt.de<http://www.kdsl.tu-darmstadt.de/>
[2] http://www.kdsl.tu-darmstadt.de/de/home/research-program/personalized-content-acquisition-from-heterogeneous-sources/
[3] http://www.kdsl.tu-darmstadt.de/de/home/research-program/preference-based-profiling/

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