The Research Training Group “Adaptive Information Preparation from
Heterogeneous Sources” (AIPHES) , which has been established in
2015 at Technische Universität Darmstadt and at Ruprecht Karls
University Heidelberg is filling several positions for three years,
starting on April 1st, 2018. Positions remain open until filled.
The positions provide the opportunity to obtain a doctoral degree in
the research area of the training group with an emphasis, e.g., in
graph-based discourse processing, in natural language processing tasks
such as automated summarization, in representation and analysis of
text-induced structures, in jointly analyzing text and images, or in a
related area. The group will be located in Darmstadt and Heidelberg.
The funding follows the guidelines of the DFG, and the positions are
paid according to the E13 public service pay scale.
The goal of AIPHES is to conduct innovative research in knowledge
acquisition on the Web in a cross-disciplinary context. To that end,
methods in computational linguistics, natural language processing,
machine learning, network analysis, computer vision, and automated
quality assessment will be developed. AIPHES will investigate a novel,
complex scenario for information preparation from heterogeneous
sources. It interacts closely with end users who prepare textual
documents in an online editorial office, and who should therefore
profit from the results of AIPHES. In-depth knowledge in one of the
above areas is desirable but not a prerequisite.
Participating research groups at Technische Universität Darmstadt are
Knowledge Engineering (Prof. Fürnkranz), Ubiquitous Knowledge
Processing (Prof. Gurevych), Machine Learning (Prof. Kersting), Visual
Inference (Prof. Roth), Algorithmics (Prof. Weihe). Participants at
Ruprecht Karls University Heidelberg are the Institute for
Computational Linguistics (Prof. Frank) and the Natural Language
Processing Group (Prof. Strube) of the Heidelberg Institute for
Theoretical Studies (HITS).
AIPHES emphasizes close contact between the students and their
advisors, have regular joint meetings, a co-supervision by professors
and younger scientists in the research groups, and an intensive
exchange as part of the research and qualification program. The
training group has the goal of publishing its results at leading
scientific conferences and will actively support its doctoral
researchers in this endeavor. The software that will be developed in
the course of AIPHES should be put under the open source Apache
Software License 2.0 if possible. Moreover, the research papers and
datasets should be published with open access models.
We are looking for exceptionally qualified candidates with a degree in
Computer Science, Computational Linguistics, or a related study
program. We expect ability to work independently, personal commitment,
team and communication abilities, as well as the willingness to
cooperate in a multi-disciplinary team. Desirable is experience in
scientific work. Applicants should be willing to work with
German-language texts, and, if necessary, to acquire German language
skills during the training program. We specifically invite
applications of women. Among those equally qualified, handicapped
applicants will receive preferential consideration. International
applications are particularly encouraged.
The Department of Computer Science of TU Darmstadt  is regularly
ranked among the top ones in respective rankings of German
universities. The Institute for Computational Linguistics (ICL) of the
Ruprecht Karls University Heidelberg  is one of the largest centers
for computational linguistics both in Germany and internationally. The
ICL and the NLP department of the HITS jointly run the graduate
program “Semantic Processing”  with an integrated research training
group “Coherence in language processing: Semantics beyond the
sentence”, which has a close connection to the topics in computational
linguistics of AIPHES.
Applications should include a motivational letter that refers to one
or two of the planned research areas of AIPHES , a CV with
information about the applicant’s scientific work, certifications of
study and work experience, as well as a thesis or other publications
in electronic form. Application materials must be submitted via the
following form until October 6th, 2017:
In addition, applicants should be prepared to solve a programming and
a reviewing task in the first two weeks after their application.