Open Thesis Topics

Students who are eager to develop their skills by doing a research-oriented thesis in our group should mail their interests to [email protected]. Suitable topic candidates are shown in the following list. Your own suggestions for topics are also welcome, for which you can draw inspiration from our recent publications.

  • Assessing the Quality of Comparative Review Webpages
  • Assessing the Robusness of Retrieval Models to Adversarial Examples.
  • Children as Searchers: Improving Web Search for Children
  • Constructing and Analyzing a diverse Corpus of AI-generated Text
  • Contrastive Ranking-Aware Learning of Representations for Retrieval
  • Does Training Data from different Corpora benefit Learning-to-Rank?
  • Explain Google Rankings via Query Reformulation
  • Evaluating Document-level Classification by Combining Sentence-level Predictions
  • Extreme Multi-Label Classification of German Book Titles
  • Facets of complexity in scholarly political language
  • Fine-granular and Web-scale Language Identification for Multi-lingual LLMs
  • Jointly Learning Decoupled Bi-Encoder Representations for Retrieval
  • Logical Features of Neural Networks
  • Measuring the Correlation of the Effectivenes of Large Language Models and Retrievability
  • Mimicking Personas of Dialog Participants with Large Language Models
  • Multi-Task Learning with IR Axioms
  • Persuasive Argument Generation using Generative Adversarial Networks
  • Personalization Strategies for LLMs to reduce Harmful Content Generation
  • Psychological Features for Argumentation
  • Query Obfuscation for Dense Retrieval Models
  • Question Analytics of Conversational QA-Systems: A Gamified Study
  • Rating the Degree of Search Engine Optimization of Websites
  • Reducing the Size of Dense Retrieval Indexes by Removing Unimportant Terms
  • Simplifying the language of political argumentation
  • Unraveling Argumentation Strategies through XAI Techniques
  • Zero-Shot Token Retrieval Using Pre-trained Language Models

Open Student Assistant Topics

Students who want to improve their skills and work with us can apply for a position as a student assistant at [email protected]. We are currently looking for assistants to work on the following topics:

  • Analyzing and Tuning Chat Bots for Emotional Support Tasks

Ongoing Theses

  • Halle
    • Max Henze. Simulation von Suchanfragen durch Anchortext (supervised by Maik Fröbe, Sebastian Günther, and Matthias Hagen)
  • Jena
    • Lukas Zeit-Altpeter. Answering Open-Ended Health-Related Questions based on Trusted Sources (supervised by Jan Heinrich Reimer, Alexander Bonarenko, and Matthias Hagen)
    • Maximilian Ernst. Re-Creating Twitter-based IR and NLP-Experiments on the Feediverse (supervised by Jan Heinrich Reimer and Matti Wiegmann)
  • Leipzig
    • Pierre Achkar. Classification of Multimodal Social Media Posts (supervised by Tim Gollub)
    • Yannick Brenning. Active Learning for Text Classification (supervised by Christian Kahmann and Christopher Schröder)
    • Nicolaus Schlegel. Incorporating Knowledge Graph Embeddings in Large Language Models (supervised by Ferdinand Schlatt)
    • Jonas Probst. Implicit Evaluation of Health Answers from Large Generative Text Models (supervised by Sebastian Schmidt, Harry Scells)
    • Dinara Imambayeva. Improving Compositionality of Images Generated by Stable Diffusion (supervised by Niklas Deckers)
    • Hassan Jbara. Text-Conditioned Generation of SVG Images (supervised by Niklas Deckers and Lukas Gienapp)
    • Janko Götze. Cross-domain Counterargument Retrieval using Large Language Models (supervised by Nailia Mirzakhmedova and Johannes Kiesel)
    • Marvin Vogel. Axiomatic Re-ranking for Argument Search (supervised by Maximilian Heinrich and Alexander Bondarenko)
    • Ruben Kohlmeyer. Probing Large Language Models for Causal Knowledge (supervised by Ferdinand Schlatt)
    • Julia Peters. Manipulating Embeddings of Stable Diffusion Prompts (supervised by Niklas Deckers)
    • Thilo Brummerloh. Extracting Large-Scale Multimodal Datasets From Web Archives (supervised by Niklas Deckers)
    • Pia Sülzle. Detecting Hidden Meaning in Stock Images (supervised by Niklas Deckers)
    • Marc-Pascal Richter. Normdaten-Disambiguierung und Reconciliation auf Korpusdaten (supervised by Erik Körner and Felix Helfer)
    • Karl Hase. Statistical Bootstrap Tests with Redundant Data (supervised by Maik Fröbe)
    • Sebastian Schmidt. Collecting Fine-grained, Intesity-aware Annotations of Triggering Content. (supervised by Matti Wiegmann and Magdalena Wolska)
  • Weimar
    • Fatihah Ulya Hakiem. Health-Related Queries in Large-Scale Query Logs (supervised by Jan Heinrich Reimer)
    • Nazifa Kazimi. Information Extraction from Academic Mailing Lists (supervised by Tim Gollub)
    • Krishna Chaitanya. Topic Segmentation with Large Language Models (supervised by Johannes Kiesel, Nailia Mirzakhmedova, and Matti Wiegmann)
    • Islam Torky. Retrieval Augmented Generation for the IR-Anthology (supervised by Tim Gollub)
    • Alban Bruder. Interacting with a Multi-User Voice Search in VR (supervised by Johannes Kiesel and Marcel Gohsen)
    • Kshitij Pandit. Mining Linked Data on Web Scale (supervised by Nikolay Kolyada)
    • Ludwig Lorenz. Searching Personal Web Archives (supervised by Johannes Kiesel)

Resources for Students


Dear prospective PhD student, unsolicited applications to the Webis group ( are welcome. However, we cannot promise that open positions are available at the time of your application.

The Webis Group is a tightly cooperating research network, formed by computer science chairs at the universities of Groningen, Hannover, Jena, Leipzig, and Weimar. Our mission is to tackle challenges of the information society by conducting basic and applied research with the goal of prototyping and evaluating future information systems. We are an experienced research group where team spirit and active collaboration has top priority. We are looking for open-minded graduates and PhDs who want to develop both as a researcher and as a person. The working language of our group is English; fluency in German is not required.

Interested students should have finished either a master or a PhD in computer science, mathematics, or a related field with excellent or very good grades. A solid background in mathematics and statistics is expected—as well as very good programming skills.

Benno Stein
Bauhaus-Universität Weimar
On behalf of the Webis group

Email: [email protected]