Assistant Professor in Applied Data Science

vor 2 Wochen

Wien, Wien, Österreich Modul University Vienna GmbH Vollzeit

Assistant Professor in Applied Data Science (all genders)

Full-time (40 h/w) in Vienna, Austria
Modul University Vienna (MU) is seeking an outstanding scholar to teach, raise external funding

and conduct research in the area of Applied Data Science with a special focus on Machine

following areas:
Machine learning, data mining, natural language processing, intelligent systems,

or recommendation systems. Additional experience in areas such as computational statistics,

analysis of the large-scale data, and analysis of data generated in online systems, as well as,

industry experience in one or more of these areas are highly appreciated. Furthermore, strong

methodological background and analytical skills are desirable.

Responsibilities
The position is located within the School of Applied Data Science and it is designed to play a

leading role in advancing the applied data science study program within the university through

innovative, high-quality research and effective teaching. Applicants should be willing to take a

leadership role in developing the School's profile through original research and knowledge

transfer activities in the area of analysis of large-scale customer and user engagement (e.g., in

complement and extend the current strengths of the University and the School. For an overview

of the current academic community of Professors at Modul University Vienna and their research

activities, see Faculty and Staff | Modul University Vienna.

programs offered by Modul University Vienna. The teaching load for this position is 5 units/week

per semester (1 unit = 45 minutes of teaching). The new colleague will be expected to further

develop her/his research agenda in the general area of machine learning and data mining, and in

particular event stream mining.

Desired skills and essential qualifications
in research and teaching, possess a comprehensive understanding of widely used concepts and

methodology of selected topics in the areas of big data analytics and analysis of Web-scale data,

and have a demonstrated ability to work effectively in interdisciplinary research projects with

university and department colleagues.

  • Doctorate in computer science, data science, business informatics, statistics, or related
fields;

  • Evidence of research potential;
  • Commitment to high quality teaching;
  • Ability to teach courses in the fields of programming, fundamentals of computer
science, databases, data mining, computational statistics, and/or related areas;

  • Ability to attract external funding to support research programs and to successfully build
collaborative research programs with other universities, research institutes and

industry;

  • Leadership skills to further develop the area of digital services at MU;
  • Excellent spoken and written English language skills, proficiency in German language is
not required but welcome.

Remuneration
This is a fixed term, full-time position with
40 working hours per week. The initial annual gross

salary is EUR 55,000 before taxes (plus approx. 30% Arbeitgeberanteil = contribution to the social

security and pension fund paid by the employer according to Austrian Law). The University offers

opportunities for additional remuneration for extra teaching and outstanding scholarships.

Contact and Application
after announcement. The expected starting date is September 2023. Please send your complete

preferably as a single PDF file not exceeding 8 MB).

For questions related to these positions please contact the Head of School of Applied Data

current research programs can be found at Faculty Publications | Modul University Vienna.

Modul University Vienna is an equal opportunity employer with a strong commitment to equality and

diversity that does not discriminate on the basis of, among other factors, age, color, disability, ethnicity,

gender or gender expression, national origin, race, sexual orientation, or social class. We especially

contribute to a diverse working culture.

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