Predoctoral assistant Computer Science (Completion Contract)

The Doctoral School Computer Science provides the option of performance-based contract extensions (up to 6 months) to doctoral students who previously had a position as university assistant (prae doc) or as third-party funded researcher (prae doc) at the University of Vienna. This completion contract is meant to support a high-qualified DoCS fellow in the final phase of the doctoral studies. The successful applicant will get a contract as university assistant (prae doc) and will be working in the research group "Visualization and Data Analysis" at the Faculty of Computer Science.

Your future tasks:

Participation in research, teaching and administration:

• Completion of your doctoral studies
• Participation in teaching and independent teaching of courses as defined by the collective agreement
• Participation in research projects / research studies
• Participation in publications / academic articles / presentations
• Supervision of students
• Involvement in the organisation of meetings, conferences, symposiums
• Involvement in the department administration as well as in teaching and research administration, * Letter of Motivation, including ideas for a prospective doctoral project proposal
• Abstract of master thesis
• Master Degree / Diploma
• List of publications, evidence of teaching experience (if available)

• Professional competence: Background in Computer Science, specifically Human-Computer Interaction, Data Visualisation, Data Science (Master's level or equivalent professional experience)
• Master's degree in one of the following areas: Human-Computer Interaction, Data Visualisation, Data Science, Information Science, Science and Technology Studies, Communication Science or a related field
• Methodological competence: First experience with qualitative research methods
• Didactic competence desired
• High ability to express yourself both orally and in writing
• Excellent command of written and spoken English
• IT user skills
• Ability to work in a team, * Teaching experience/experience of working with e-learning
• Knowledge of university processes and structures
• Experience abroad
• Basic experience in research methods and academic writing
• Experience with data
• Good programming skills (Python / javascript / D3 / C++)
• Tableau
• Background in or experience creating data visualizations
• Experience with quantitative research methods (e.g., in data science or statistical knowledge)
• First experiences of interdisciplinary collaborations
• Teaching or tutoring experience or workshop facilitation

Fair salary: The basic salary of EUR 3.776,10 (on a full-time basis) increases if we can credit professional experience.

At the University of Vienna, over 10,000 people work together on the big questions of the future. Approximately 7,500 of them are academic staff members. These are individuals who, with their curiosity and their continuous pursuit of excellence, engage in international cutting-edge research and teaching. With us, you will also find space to unfold your potential. We are looking for a/an, Among the many good reasons to want to research and teach at the University of Vienna, there is one in particular, which has convinced around 7,500 academic staff members so far. They see themselves as personalities who need space for their continuous striving and curiosity in order to be able to be More than 7,500 academics at the University of Vienna thrive on continuous exploration and curiosity and help us better understand our world. Does this sound like you? Then join our accomplished team!, The Doctoral School Computer Science DoCS (https://docs.univie.ac.at), founded in 2020, offers a structured doctoral programme that supports early-career research in the areas of Computer Science and Business Informatics. The programme includes world-leading researchers in Computer Science as supervisors and co-supervisors who pursue basic as well as applied research within an intensive doctoral teaching programme tailored to the scientific needs of students in small topical groups.