We welcome Master’s students who are looking for thesis opportunities in natural language processing and related fields to work with us. If you are enthusiastic about solving real-world challenges with NLP, particularly in the areas of multilingual and multimodal systems, or exploring societal impacts such as mental healthcare applications, consider joining our team.

What We Offer:

  • Supervision and guidance from leading experts in NLP and AI.
  • Access to state-of-the-art research tools, datasets, and computational resources.
  • Opportunities to publish in high-impact venues and present at conferences.

Eligibility Criteria:

  • Must be enrolled in a Master’s program at TU Darmstadt.
  • Strong academic background in computer science, machine learning, natural language processing or a related field.
  • Experience or coursework in natural language processing is preferred.

How to Apply:

  • A brief description of your research interests and how they align with our work.
  • Your CV and academic transcript.

Please send your application to shaoxiong.ji@tu-darmstadt.de


Previous MSc Thesis Topics

  • Henna Roinisto (MSc, University of Helsinki, jointly with Metsä Group)
    Integrating Open-Source Retrieval-Augmented Generation with Large Language Models for Business, Market and Responsibility Insights, 2024.

  • Ya Gao (MSc, Aalto University, now PhD candidate at Aalto University)
    Joint entity and relation extraction via contrastive learning on knowledge-augmented graph embeddings, 2023.

  • Tuulia Denti (MSc, Aalto University, jointly with HUS, now Data Analyst at HUS)
    Natural Language Processing with Topic Models for Clinical Texts of Prostate Cancer Patients, 2022.

  • Wei Sun (MSc, Aalto University, jointly with HUS, now PhD candidate at KU Leuven, Belgium)
    Extracting Medical Entities from Radiology Reports with Ontology-based Distant Supervision, 2022.

Previous MSc Research Projects

  • An empirical study of language modeling and translation as multilingual pretraining objectives (2023-24 at University of Helsinki)
  • Deep learning for medical code assignment from clinical notes (2020-2022 at Aalto University)
  • Deep model fusion in federated learning (2020-2021)
  • Conversational/multimodal sentiment analysis (2020-2021 at Aalto University)
  • NLP for mental health (e.g, depression detection and suicidal ideation detection) (2021-2023 at Aalto University)
  • Adverse drug event detection and extraction (2021-2022 at Aalto University)
  • Multilingual complex named entity recognition at SemEval shared tasks (2021-2022 at Aalto University)
  • Risk adjustment for healthcare plan payment (2019-2020 at Aalto University)

Previous BSc Thesis Topics

  • Risk adjustment for health plan payment (2019 Winter at Aalto University)
  • Deep learning for cyberbullying detection (2020 Summer at Aalto University)
  • Pretrained language models for diagnosis code prediction (2020 Summer at Aalto University)
  • Federated learning (2020 Fall at Aalto University)
  • Depression detection from social content (2021 Spring at Aalto University)
  • Biomedical text classification (2022 Spring at Aalto University)

Published Project Reports

We are proud of our students’ exceptional research, which has played a key role in advancing language technology for societal impact. Through their dedication, they have co-authored papers in top scientific venues, developed novel NLP models, and contributed to impactful, real-world projects. Their work reflects the collaborative environment we foster. Below is a list of project reports that were published after revisions in scientific venues.




Photo by Patrick Tomasso on Unsplash