We are currently reorganising our job portal. During the transition phase, you can find more jobs here: previous DLR job portal
Apply now
Student Computer Science (f/m/d) - Integrating LLM into STT for output correction
Job Description
Req ID:  717
Place of work:  Braunschweig
Starting date:  19.02.2025
Career level:  Student research project and final thesis
Type of employment:  Full-time, Part time
Duration of contract:  bis zu 6 Monate

Remuneration: Remuneration is in accordance with the Collective Agreement for the Public Sector - Federal Government (TVöD-Bund)

Enter the fascinating world of the German Aerospace Center (Deutsches Zentrum für Luft- und Raumfahrt e. V.; DLR) and help shape the future through research and innovation! We offer an exciting and inspiring working environment driven by the expertise and curiosity of our 11,000 employees from 100 nations and our unique infrastructure. Together, we develop sustainable technologies and thus contribute to finding solutions to global challenges. Would you like to join us in addressing this major future challenge? Then this is your place!

The DLR Institute of Software Technology sees software as a catalyst for research and innovation. As experts in software, we research and develop cutting-edge solutions in all application areas of DLR.

Our areas of competence include reliable and safety-critical software systems, artificial intelligence, high-performance computing and quantum computing, human-system interaction and visualisation, software and systems engineering as well as digital platforms and digital twins.

The institute’s staff of currently around 200 employees is already contributing to tomorrow's innovations in aviation, aerospace, energy, transport and safety through their research and development of state-of-the-art software solutions.

 

What to expect

Concurrent Engineering (CE) allows different disciplines—structural design, avionics, software—to work in parallel, making collaboration essential. Engineers must balance trade-offs, ensure smooth integration, and communicate effectively. Large Language Models (LLMs) can support this process by analyzing discussions, refining documentation, and assisting with decision-making. Our Modeling and Simulation group is developing a system that combines Speech-to-Text (STT) and LLM technologies to capture and process technical discussions in Concurrent Engineering Facilities (CEFs)

 

Your tasks

Your master’s thesis will focus on:


1.    Literature Review – Investigate how well STT models perform in technical environments, particularly in aerospace. Analyze their handling of technical jargon, common errors, and correction methods. Explore how LLMs and domain-specific dictionaries can improve accuracy.


2.    Implementation – Develop a small-scale demonstration to test an STT model in a simulated technical discussion. Evaluate its performance, identify limitations, and integrate LLM-based correction mechanisms to enhance transcription quality.

 

Your profile

  • proficiency in Programming Languages: Experience in Python is essential.
  • knowledge of Neural Networks: Basic understanding of neural networks and familiarity with deep learning frameworks such as PyTorch, TensorFlow  and Keras
  • data Manipulation Skills: Experience with Pandas and NumPy for data analysis and manipulation
  • familiarity with Large Language Models (LLMs): Basic understanding of LLM architectures, prompt engineering, and common challenges with their application
  • strong English Proficiency: Good communication skills, both written and spoken
  • self-Motivation and Independence: Ability to work independently, take ownership of tasks, and solve problems proactively
  • resilience and Risk-Taking: Willingness to experiment, learn from failures, and iterate on solutions

 

We offer

DLR stands for diversity, appreciation and equality for all people. We promote independent work and the individual development of our employees both personally and professionally. To this end, we offer numerous training and development opportunities. Equal opportunities are of particular importance to us, which is why we want to increase the proportion of women in science and management in particular. Applicants with severe disabilities will be given preference if they are qualified.

We look forward to getting to know you!

 

If you have any questions about this position (Vacancy-ID 717) please contact:

 

Andreas Gerndt 
Tel.: +49 531 295 2782 
Apply now