Linguistics - Natural Language Processing
How do you build Large Language Models? How do humans experience Natural Language Processing (NLP) applications in their daily lives? And how can we design language technologies that are transparent, trustworthy, and capable of capturing nuance—whether it is subjective framing, subtle translation errors, or emerging forms of misinformation?
Natural Language Processing is at the core of today's digital world. Machine Translation supports multilingual communication, conversational agents and advanced search systems help us navigate information, and generative AI assists with tasks ranging from creative writing to data analysis. Research in NLP is essential for making these technologies more accurate, responsible, and aligned with human needs.
Our two-year Research Master in Natural Language Processing offers rigorous, interdisciplinary training that integrates Linguistics and Computer Science. You will gain strong technical skills in machine learning, language modelling, text processing, and computational semantics, together with a deep understanding of human language, linguistic theory, and experimental research methods. The programme prepares you to contribute to cutting-edge research in academia or industry, and to critically evaluate the social and ethical impact of NLP technologies.
Prospective students with a Bachelor's degree in Linguistics or another relevant Bachelor's degree are admissible to this master's track. Examples of these types of Bachelor's programmes are:
- Computer Science
- (Computational) Linguistics
- Artificial Intelligence
- Digital Humanities
- Language Technologies
- Cognitive Science
- Mathematics, or relevant disciplines
- Reference letter: Names and contact of two referees;
- Curriculum Vitae (CV)
- Motivation letter
Job prospects
After graduation, you are well prepared for a career in Information Technology. You can work on subjects such as machine translation, knowledge and data engineering, expert in Large Language Models and Explainable AI, human-computer dialogue systems, machine learning engineer, computational linguist, among others.
You can also continue your career in research, for instance as a PhD student at a university.
Job examples
- Language engineer
- Data Analyst/ Data Scientist
- Software engineer
- Data Manager
- Knowledge engineer
- Computational Linguist
- Data Linguist
