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Student Computer Science (f / m / d) - Integrating LLM into STT for output correction - Forschung, Ingenieur

Deutsches Zentrum für Luft- und Raumfahrt (DLR)Braunschweig, Germany

Vor 30+ Tagen

Stellenbeschreibung

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 :

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.
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 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

Jobalert für diese Suche erstellen

Computer Science • Braunschweig, Germany