The University of Vienna (20 faculties and centres, 179 fields of study, approx. 10.000 members of staff, about 90.000 students) seeks to fill the position from 15.10.2021 of a

University Assistant (prae doc)
at the Research Group Visualization and Data Analysis


Reference number: 12127

The research group Visualization and Data Analysis (Univ.-Prof. Dr. Torsten Möller, Dr. Laura Koesten) at the Faculty of Computer Sciences invites applications for the position of a research assistant aiming at a PhD degree. We seek a highly motivated PhD researcher with a background in computer science or a related field.
We expect a high motivation to learn and engage with real world data, people and problems in an interdisciplinary setting. The successful applicant has the opportunity to propose their own project idea or to work in collaboration with the project “Transparent and explainable models”, funded by the Vienna Science and Technology Fund (WWTF) described below.
The applicant should be confident in engaging with different audiences and bring motivation to write scientific papers for journals and conferences. There will be the possibility to collaborate with other students.
We offer a pleasant work environment within a friendly, dynamic, international and young team in Vienna, one of the cities with the highest quality of life worldwide. The working language is English, and we are committed to diversity and inclusion. There are many opportunities to grow academically as well as personally, including the opportunity to act as possible team leads in research projects, engage in exchanges on an international scale and develop contacts to industry. We provide a close and supportive supervision of the thesis work and a highly collaborative research environment. There is an option to extend the contract after the PhD defense in the context of further academic training.
The University of Vienna intends to increase the number of women on its faculty, particularly in high-level positions, and therefore specifically invites applications by women.
Your application: Applications including a letter of motivation (English or German), your curriculum vitae (CV), a list of publications and teaching experience (if applicable) and copies of degree certificates should be submitted via the Job Center to the University of Vienna (http://jobcenter.univie.ac.at), mentioning the reference number. We strongly encourage people from underrepresented groups to apply.
For further information please contact Torsten Möller / Laura Koesten




Duration of employment: 4 year/s

Extent of Employment: 30 hours/week
Job grading in accordance with collective bargaining agreement: §48 VwGr. B1 Grundstufe (praedoc) with relevant work experience determining the assignment to a particular salary grade.

Job Description:
This role is for a fixed duration of 48 months.
- Participation in research, teaching and administration
- Participation in research projects / research studies
- Participation in publications / academic articles / presentations
- We expect the successful candidate to sign a doctoral thesis agreement within 12-18 months.
- Participation in teaching and independent teaching of courses as defined by the collective agreement
- Supervision of students
- Involvement in the organisation of meetings, conferences, symposiums
- Involvement in the department administration as well as in teaching and research administration


About the project: Transparent and explainable models

In data science, models are usually created by experts in modeling (i.e. data analysts) and then used by domain experts (such as astronomers, chemists, social scientists, etc.). This grant is addressing two major challenges in this process. On the one hand, while many models are validated by computing their performance on given data, it often remains unclear exactly why they work. On the other hand, it is well known that there are many more people with modeling needs (that have data and a set of questions to be answered) than there are people with the needed modeling skills. Hence, we are developing a set of guidelines on how to create tools to close that knowledge gap between model builders and model users. In this grant, we will not be able to address all types of modeling. We focus on two of the most difficult approaches: (a) clustering techniques, independent of a specific algorithm, (b) deep neural networks, independent on the analysis need. In addition, we will develop a general purpose approach, informed by a decade long experience in visual parameter space analysis. All three approaches are complementary and allow us to avoid pitfalls in any single of these approaches. The validation of our results will be done in three different levels: (1) using benchmark data sets, (2) working with very specific applications in astronomy, medicine, and finance, (3) through an iterative design process in collaboration with expert algorithmic modelers as well as domain scientists.


Profile:
- Completed Masters degree preferably in computer science or a related scientific field
- Professional competence (Bachelor’s level or equivalent professional experience): Background in Computer Science; specifically Machine Learning, Data Visualisation, Data Science, Statistics or a related field
- Methodological skills: First experience with ML/Data Mining, possibly Image Analysis
- Technical skills: Ability to quickly implement software prototypes
- Didactic skills
- Presentation skills
- Excellent communication skills
- Excellent command of written and spoken English
- IT user skills
- Ability to work in a team


Application documents:
- Letter of Motivation including ideas for a prospective doctoral project proposal
- Curriculum vitae
- List of publications, evidence of teaching experience (if available)
- Degree certificate
- Transcripts of all Bachelor und Master degree grades



Desirable qualifications are
- Masters degree in one of the following areas: Informatics / Computer Science specifically Data Visualisation, Data Science, Computational Science, Visual Computing, Bioinformatics, Statistics, or a related field
- Teaching experience / experience of working with e-learning
- Knowledge of university processes and structures
- Experience abroad
- Good German language skills
- Basic experience in research methods and academic writing
- Experience with data visualisation (e.g. D3.js, Tableau, matplotlib)
- Experience with ML frameworks like Pytorch, Tensorflow, scikit-learn
- Technical skills in GPGPU programming (CUDA or OpenCL)
- Knowledge in statistics
- Excellent programming skills in one language (Python / R / C++)
- 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



Research fields:
Main research field
Special research fields Importance
Computer Sciences
Human-computer interaction;Data science;Visualisation MUST


Education:
Educational institution
Educational level Special subject Importance
University
Mathematics, Computer Sciences - SHOULD


Languages:
Language
Language level Importance
English
Very good knowledge MUST


Computer-Skills:
Type of computer skills
Specified computer skills  Importance
Programming language
Others SHOULD


Applications including a letter of motivation (German or English) should be submitted via the Job Center to the University of Vienna (http://jobcenter.univie.ac.at) no later than 15.10.2021, mentioning reference number 
12127.

For further information please contact Koesten, Laura +43-1-4277-79020.

The University pursues a non-discriminatory employment policy and values equal opportunities, as well as diversity (http://diversity.univie.ac.at/). The University lays special emphasis on increasing the number of women in senior and in academic positions. Given equal qualifications, preference will be given to female applicants.

Human Resources and Gender Equality of the University of Vienna
Reference number: 12127
E-Mail: jobcenter@univie.ac.at
Privacy Policy of the University of Vienna


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