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Open Position

Open Thesis

Finished Thesis

Open Position

Research Assistant Opportunity: LLM & Knowledge Graphs

We are excited to announce an opening for a Research Assistant position focused on Language Models (LLM) and Knowledge Graphs, commencing in November. Join our dedicated team and be at the forefront of cutting-edge research in this rapidly evolving domain.

  • Role: Research Assistant

  • Duration: Starting asap (Flexible start date) 

  • Commitment: 19 hours/week

Required Skills:

  • Basic understanding of transformer models such as BERT, GPT, and T5.

  • Familiarity with Knowledge Graphs and their integration with language models.

  • Familiarity in using libraries like Hugging Face’s Transformers and PyTorch.

  • Hands-on experience with LangChain or a strong desire to learn.

  • A grasp on fine-tuning techniques and practices for large language models.

  • Strong analytical skills and attention to detail.

Key Responsibilities:

  • Assist in designing, implementing, and evaluating experiments related to LLM and Knowledge Graphs.

  • Dive deep into transformer architectures, understanding their intricacies, and optimizing their performance.

  • Work with LangChain, fine-tuning, and other related areas to ensure the smooth integration of LLMs into our existing systems.

Research Vacancies

Manual and automated detection of bias in medical data of ICUS

This research project is associated with IntelliLung project which aims to reduce lung injuries by providing automated suggestions to clinicians for mechanically ventilated patients in the ICU. Biased datasets can degrade the performance of the trained algorithms. Considering the importance of safety for this application, it is critical to identify these biases, discover their source and develop strategies to mitigate them. Also the aim is to discover biases that can help us structure and reduce complexity of the algorithms. This project is supported by interdisciplinary teams and aims to identify biases not only from the Machine Learning (ML) / Reinforcement Learning (RL) perspective but also using the domain knowledge provided by clinicians.

Your responsibilities

  • Data analysis of the existing datasets (e.g. MIMIC-IV) to identify any issues that might affect the performance of the algorithms.

  • Identify detectable biases within the project’s context and develop methods leveraging knowledge from ML/RL algorithms to uncover and address them.
  • Translate clinicians’ expertise into a set of actionable rules for detecting biases from a medical standpoint.

  • Identify and utilize appropriate tools and methodologies (e.g. statistical methods, explainable AI) to support the project’s objectives.

  • Leverage insights from the analysis to enhance offline evaluation processes.


  • Enrolled in a Master program in computer science or related field

  • Already extensive experience with data preparation, analysis, visualization and relevant tools

  • Good knowledge of ML algorithms and tools (i.e pytorch, numpy, pandas etc), knowledge of RL is a plus

  • Good written and verbal communication skills (in English)

  • Motivated to do independent research.

In case of any questions or interest, please contact by sending your CV and study transscript:

Mohammad Hamza:

Sahar Vahdati:

Open Thesis

Determinantal Point Processes for Prompt Engineering for LLMs

The performance of a large language model (LLM) is sensitive to the way it is prompted. Automated prompt engineering methods aim to find suitable prompts for a given task by sampling several prompts and evaluating them. Existing automatic prompt engineering methods do not generate sufficiently diverse sample prompts or rely on several meta-prompting tricks to achieve the desired results. In this thesis, we will use a method for prompt selection to directly optimise diversity and estimated performance by exploiting so called determinental point processes. The thesis will involve comparisons of this technique to state-of-the-art prompt engineering methods such as PromptBreeder from DeepMind.


  • Excellent and long standing interest and knowledge in mathematics

  • Good programming skills in Python and PyTorch (optional)

Finished Thesis

Design and Development of Murphy System: Generating Meaningful Negative Samples for KGEs

Ali, Semab

Design and Development of Murphy System: Generating Meaningful Negative Samples for KGEs Masters Thesis

University of Bonn, Germany, 2022.

BibTeX | Tags: FinishedThesis | Links:

Going Beyond the Paradigm of Knowledge Graph Embedding Models

Kumar, Abishek

Going Beyond the Paradigm of Knowledge Graph Embedding Models Masters Thesis

University of Bonn, 2022.

BibTeX | Tags: FinishedThesis | Links:

Unveiling the Effect of using Moebius Transformations on Knowledge Graph Embeddings

Aykul, Can

Unveiling the Effect of using Moebius Transformations on Knowledge Graph Embeddings Masters Thesis

University of Bonn, Germany, 2020.

BibTeX | Tags: FinishedThesis | Links:

How to Apply

If interested please send your CV to Dr. Sahar Vahdati: