PhD Position in Data Science Modeling Digital Language Diversity, Austria

  •  PhD
  •  16-May-2025
  •   Austria
  • $$  Funded position (30 hours/week for 30 months, with possible extension)

Achieve your educational goals with the help of the PhD Position in Data Science Modeling Digital Language Diversity offered by TU Wien (Vienna University of Technology). The bursary is open for the academic session 2025-2026.

To be eligible for this PhD position, applicants must hold a completed Master’s degree in Computer Science, Data Science, Computational Linguistics, or a related field. They should have strong programming skills in Python, with experience in data processing, natural language processing (NLP), and machine learning. Familiarity with large-scale text data, language modeling, or language identification is required, along with proficiency in data analysis and statistical modeling.

TU Wien (Vienna University of Technology) is one of Europe’s leading institutions for research, technology, and innovation. Located in the heart of Vienna, Austria, TU Wien is known for its strong focus on engineering, computer science, and natural sciences. With a vibrant international community and a commitment to interdisciplinary collaboration, TU Wien offers cutting-edge facilities and a dynamic environment for students and researchers.

Technische Universität Wien Information

Technische Universität Wien Grants

PhD Position in Data Science Modeling Digital Language Diversity, Austria Established in 1815, Technische Universität Wien (Vienna University of Technology) is a non-profit public higher education institution located in the urban setting of the large city of Vienna (population range of 1,000,000-5,000,000 inhabitants). Officially accredited and/or recognized by the Bundesministerium für Bildung, Wissenschaft and Forschung, Österreich (Federal Ministry of Education, Science and Research, Austria), Technische Universität Wien (TU Wien) is a large (uniRank enrollment range: 25,000-29,999 students) coeducational higher education institution. Technische Universität Wien (TU Wien) offers courses and programs leading to officially recognized higher education degrees in several areas of study. See the uniRank degree levels and areas of study matrix below for further details. This 204 years old higher-education institution has a selective admission policy based on entrance examinations. International applicants are eligible to apply for enrollment.

Eligibility Criteria

  • Eligible Countries: All nationalities

  • Acceptable Course or Subjects: The scholarship will be awarded in the field of Data Science, Computational Linguistics, or closely related areas.

  • Admissible Criteria: To be eligible, applicants must meet all the following criteria:

    • Hold a completed Master’s degree in Computer Science, Data Science, Computational Linguistics, or a related discipline

    • Demonstrate strong proficiency in Python, with experience in NLP, data processing, and machine learning

    • Have familiarity with large-scale text data, language modeling, or language identification

    • Show competence in data analysis and statistical modeling

    • Experience with web data (e.g., Common Crawl, social media APIs) is a plus

    • Display interest in topics such as digital inequality, language technology, or cultural analytics

    • Possess excellent communication and collaboration skills in English.

Offered Benefits

  • A funded PhD position (30h/week for 30 months) with the possibility of extension

  • Supervision by experts from TU Wien (Computer Science) and the University of Vienna (Linguistics)

  • Involvement in the Vienna Doctoral College on Digital Humanism

  • Access to a highly collaborative, interdisciplinary, and flexible research environment

  • The chance to live and work in Vienna, consistently ranked as one of the world’s most livable cities.

Application Process

Applicants must send a single PDF file to ? digilingdiv-application[ät]ds-ifs.tuwien.ac.at

The PDF must include:

  • Motivation letter (1–2 pages)

  • Curriculum Vitae (CV)

  • Academic transcripts

  • List of publications (including Master’s thesis)

  • (Optional) Link to code samples or GitHub projects

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