PhD positions in DFG Graduate School AIPHES, TU Darmstadt: Deep Learning for Structured Summaries and Abstractive Summarization

PhD positions in DFG Graduate School AIPHES, TU Darmstadt: Deep Learning for Structured Summaries and Abstractive Summarization

The Research Training Group “Adaptive Information Preparation from Heterogeneous Sources” (AIPHES) [1], which has been established in 2015 at Technische Universität Darmstadt and at Ruprecht Karls University Heidelberg is filling two positions for three years, starting as soon as possible, located in Darmstadt and associated with UKP Lab (Prof. Iryna Gurevych). Positions remain open until filled.

The positions provide the opportunity to obtain a doctoral degree with an emphasis in natural language processing tasks such as structured summaries of complex contents, abstractive summarization, or a related area. Applicants should be willing to work on cross-lingual, cross-modality and domain-independent methods. Prior experience in transfer learning, multi-task learning, adversarial learning, deep reinforcement learning or related methods is a plus.

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, computer vision, and data and information management will be developed. AIPHES investigates 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 benefit from the results of AIPHES. In-depth knowledge in one of the above areas is desirable but not a prerequisite.

AIPHES emphasizes close contact between the students and their advisors with 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. Participating research groups at Technische Universität Darmstadt are Knowledge Engineering (Prof. Fürnkranz), Ubiquitous Knowledge Processing (Prof. Gurevych, Dr. Claudia Schulz), 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 strives to publish its results at leading scientific conferences and is actively supporting 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, Machine Learning, NLP, or a related study program. We expect the ability to work independently, personal commitment, team and communication abilities, as well as the willingness to cooperate in a multi-disciplinary team. Prior experience in scientific work is a plus. We specifically invite applications of women. Among those equally qualified, handicapped applicants will receive preferential consideration. International applications are particularly encouraged.

The research environment is excellent. The Department of Computer Science of TU Darmstadt [2] is regularly ranked among the top ones in respective rankings of German universities. UKP Lab is a highly dynamic research group committed to top-level conferences, technologies of the highest standards, cooperative work style and close interaction of team members [3]. Its BMBF-funded Centre for the Digital Foundation of Research in the Humanities, Social, and Educational Sciences (CEDIFOR) emphasizes NLP, machine learning and text mining. The large-scale argument mining project allows searching large document collections in response to a user-defined topic: neural networks determine relevant pro and con arguments in real-time, and represent them in a concise summary. [4]

Applications should include a motivational letter that refers to one of the planned research areas of AIPHES [1], 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 by June, 27th, 2018:

In addition, applicants should be prepared to solve a programming and a reviewing task in the first two weeks after their application.

[1] (cf. Guiding Themes B1, B2)