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Job-Art: Wissenschaftliche Jobausschreibungen

University Assistant (prae doc) at the Department of Analytical Chemistry

Computer Sciences, Bioinformatics
Department of Analytical Chemistry, Faculty of Chemistry, University Vienna


The University of Vienna (20 faculties and centres, 178 fields of study, approx. 9.800 members of staff, about 90.000 students) seeks to fill the position from 15.03.2021 of a


University Assistant (prae doc)

at the Department of Analytical Chemistry


Reference number: 11821


Our highly motivated team is looking for a prae doc with experience in bioinformatics, biostatistics, computational biology or similar at the Institute of Analytical Chemistry. Our research focuses on elemental mass spectrometry (ICP-MS) in combination with imaging techniques and single cell methods. The successful candidate will work with interdisciplinary teams (analytical chemists, clinical, biostatistics and bioinformatics scientists) and focus on data evaluation of Mass Cytometry data sets. This method produces large amounts of raw-data, which ideally is evaluated using advanced statistical and/or machine learning method to automate this time- and resource-intensive labor as well as to greatly standardize the evaluation procedure itself. You will evaluate existing tools and methods available for processing large Mass Cytometry datasets and then develop/implement easy-to-use, reliable and automated workflows and data processing pipeline to process such data sets.


Duration of employment: 4 years

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:

Participation in research, teaching and administration:

Accomplishment of research projects in the area of bioinformatics at the Institute of Analytical Chemistry. More specifically, you will

- develop statistical/machine learning methods/tools for the processing of mass cytometry data sets;

- implement reusable data processing pipelines using the developed pipelines;

- establish and apply the developed workflows to multiple mass cytometry projects;

- teach courses independently to the extent specified in the Kollektivvertrag;

- participate in publications / academic articles / presentations;

- sign a doctoral thesis Agreement within 12-18 months;

- supervise students;

- be involved in the organisation of meetings, conferences, symposiums;

- be involved in the department administration as well as in teaching and research administration.



- Diploma or master degree in (bio-)informatics, (bio-)statistics, computational biology or similar, or equivalent qualification

- Strong expertise in Python, R and or C/C++

- Experience in using clustering techniques and dealing with high-dimensional data sets

- Experience in machine learning

- Supervision of practical courses

- 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

- Experience in high-performance computing

- Experience with Mass Spectrometry and/or Flow Cytometry are considered a plus

- The successful candidate will also be a team player with strong communication, data presentation, visualization and written skills. Enthusiasm for learning more in this field.

- Teaching experience / experience of working with e-learning

- Knowledge of university processes and structures

- Experience abroad

- Basic experience in research methods and academic writing


Application documents:

- Letter of Motivation incl. research interests

- Curriculum vitae

- List of publications, evidence of teaching experience (if available)

- Degree certificates


Research fields:

Main research field: Computer Sciences

Special research fields: Bioinformatics

Importance: must



Educational institution: University

Educational level: Mathematics, Computer Sciences

Special subject: Bioinformatics

Importance: must




Language level: Very good knowledge

Importance: must


Language level: Very good knowledge

Importance: must



Type of computer skills: Basic Knowledge / Basic Knowledge

Specified computer skills: MS Office / others

Importance: must / must


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 07.03.2021, mentioning reference number 11821.

For further information please contact Köllensperger, Gunda +43-1-4277-52303, Hilbert, Andrea +43-1-4277-52301, Zanghellini, Jürgen +43-1-4277-52306.


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: 11821

E-Mail: jobcenter(at)univie.ac.at

Privacy Policy of the University of Vienna


Name: Jobcenter of the University Vienna

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