Predoctoral assistant Computer Science

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!

Your personal sphere of influence:

As a university assistant prae doc, you will complement the research team around Prof. Torsten Möller and take over tasks like researching, teaching, collaborating with your fellows. The focus of our research is Visualization and Data Analysis. Our Team VDA currently consists of 10 members: a post doc, prae docs and administrative colleagues. What makes us special as a team is a close collaborative working and researching situation; what is particularly important to us is a healthy feedback loop within our group. The employment duration is 4 years. Initially limited to 1.5 years, the employment relationship is automatically extended to 4 years if the employer does not terminate it within the first 12 months by submitting a non-extension declaration.

Your future tasks:

You actively participate in research, teaching & administration, which means:

• You are involved in research projects and scientific studies.
• We expect you to finalize your dissertation agreement within 12-18 months.
• You work on your dissertation and its completion.
• You hold courses independently within the scope of the provisions of the collective bargaining agreement.
• You take on administrative tasks in research, teaching and administration.
• The research should focus on Human-Computer-Interaction, Visualization, Usability Research and Data Science.

This is part of your personality:

• Professional competence: Background in Computer Science; specifically Human-Computer Interaction, Data Visualisation, Data Science (Master’s level or equivalent professional experience)
• Masters 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

Desirable qualifications are

•     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

What we offer:

Work-life balance: Our employees enjoy flexible working hours and can partially work remotely. 

Inspiring working atmosphere: You are a part of an international academic team in a healthy and fair working environment.

Good public transport connections: Your workplace is easily accessible by public transport.

Internal further training & Coaching: Opportunity to deepen your skills on an ongoing basis. There are over 600 courses to choose from – free of charge.

Fair salary: The basic salary of EUR 3578,80 (on a full-time basis) increases if we can credit professional experience.

Equal opportunities for everyone: We look forward to diverse personalities in the team!

It is that easy to apply:

• With your scientific curriculum vitae
• 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)