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  3. Postdoc position in Capturing Bias in Online Media

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Postdoc position in Capturing Bias in Online Media

The role of the VU in this project is to bring expertise in the area of crowdsourcing, human computation and knowledge engineering in the context of video and text processing of online media (building on ...

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De Boelelaan, Amsterdam, Noord-Holland
Tijdelijk contract / Tijdelijke opdracht
Uren per week:
40 uur
€ 2588 - € 4084 per maand


The role of the VU in this project is to bring expertise in the area of crowdsourcing, human computation and knowledge engineering in the context of video and text processing of online media (building on results from past projects, e.g. CrowdTruth, SealincMedia, ControCurator and Web Data Quality projects). Moreover, it will also contribute with its expertise in linked data enrichment, and with its expertise in user-centred design practices. We also expect the postdoc to help with education, for example by supervising master projects related to the topics of the Capturing Bias project.

The Postodoc in this project will focus on bias- and diversity-aware accuracy measures and methods and algorithms for micro- and macro-level analysis. The Postodoc will be working in a close collaboration with the postdoc position at TUDelft within the same project, as well as with the social sciences and humanities scholars from the Leiden University (FGGA,Institute of Public Administration) and Utrecht University (Department of Media and Culture Studies) also participating in this project The Postdoc will be part of the User-Centric Data Science group ucds.cs.vu.nl/


To fill the PostDoc position for this project, we will be looking for an enthusiastic candidate with a strong motivation and the ability to proactively tackle the project’s research questions and do experimental research. The candidates should have a PhD in either computer science, information science or artificial intelligence, and have an outspoken interest in social sciences and/or humanities (in terms of users, problems, applications and content). Experience with data modelling (linked data) and data analysis techniques, in particular data science, is considered as an advantage. The candidate must be proficient in English.


The appointment will be initially for a period of 1 year with the possibility of a 2 years extension .

You may find information about our excellent fringe benefits at www.workingatvu.nl.
• Remuneration of 8.3% end-of-year bonus and 8% holiday allowance;
• Participation in a solid pension scheme (ABP);
• A minimum of 29 holidays in case of full-time employment.

The salary will be in accordance with university regulations for academic personnel, and depending on experience, range from a minimum of € 2588,00 gross per month up to a maximum of € 4084,00 gross per month (salary scale 10 CAO NU) based on a full-time employment.

Additional information

The project, which will host this Postdoc position is an interdisciplinary collaboration between computer sciences and humanities and social sciences. It is called Capturing Bias: Diversity-aware Computation for Accurate Big Media Data Analysis and is a research project funded by the Dutch National Science Foundation (NWO), within the context of the national research agenda (NWA Starting Impulse). The project focuses on providing bias- and diversity-aware methods & tools to support accurate analysis and interpretation of big media data over time. Following is a short description of the project:

News, interviews, talk shows often appear biased to citizens with different political orientations and understood differently by public policy experts and broader public. Big data analyses of such sources would be more useful if bias is captured more accurately. Detecting fake news or propaganda messages from biased media sources would be improved, potentially allowing viewers to detect misrepresentations as they watch. We aim at achieving reliable and explainable big data analysis of media related collections. Capturing Bias project will develop a framework of models for bias- and diversity-aware accuracy measures (with confidence scores and thresholds to assess reliability) and diversity-driven human computation methods for continuous gathering of opinion- and perspectives-aware training data (using crowdsourcing with citizens and ‘nichesourcing’ with experts) to support macro-level analysis, e.g role of political and gender bias in media programmes & micro-level analysis, e.g. close reading of specific programmes, bias effects of speaker selection and topic presentation. The project will tackle the challenge of accuracy by seeking to capture the diverse range of inherent diverse opinions and biases of citizens and experts in big data analysis. Scientific challenges are (1) measuring accuracy in diverse interpretation space and (2) visualizing the complexity of the interpretation space to support responsible data analytics.

For additional information please contact:
Prof. dr. Lora Aroyo
phone: +31 620329972
e-mail: Lora.Aroyo@vu.nl

Department of Computer Sciences: http://www.cs.vu.nl/.

Applicants are requested to write a letter in which they describe their abilities and motivation, accompanied by a curriculum vitae and two or three references (names and e-mail addresses).

Applications should be sent by email before February 15, 2018.
Please mention the vacancy number in the e-mail header.

Any other correspondence in response to this advertisement will not be dealt with.


Vrije Universiteit Amsterdam (VU) is a leading, innovative and growing university that is at the heart of society and actively contributes to new developments in teaching and research. Our university has ten faculties, and provides work for over 4,500 staff and scientific education for more than 23,000 students.

Research at the Faculty of Sciences focuses on the areas of Life & Health, Networked World, Fundamentals of Science, and Energy & Sustainability. The faculty’s teaching activities are directly linked to this excellent research. Cooperation with other faculties on the Vrije Universiteit Amsterdam campus generates attractive, high-quality Bachelor’s and Master’s programmes. At international level, the faculty cooperates closely with leading scientific institutes and other partners.

The VU Department of Computer Science has approximately 170 members, including 35 tenured staff members and 40-50 PhD students. The tenured staff members form the essential basis for the functioning of the department. A tenured staff member is required to make significant contributions to the research and education programmes, and to spend effort on specific tasks at the department level.

The VU Department of Computer Science hosts one of the leading Semantic Web research clusters worldwide. The User-Centric Data Science group (led by Prof. dr. Lora Aroyo, ucds.cs.vu.nl/) is part of this Semantic Web cluster and has a specific focus on human computation, crowdsourcing and collective intelligence, user-centered design practices, semantic technologies for modeling user and context for recommendation systems and personalized access of online multimedia collections, e.g. cultural heritage collections, multimedia archives and interactive TV. Researchers from this group have delivered key contributions to internationally renowned methodologies, frameworks and languages for knowledge engineering, intelligent user interfaces and Semantic Web technologies. They have been active over the years in a wide range of national and European efforts including, recently, ControCurator, SealincMedia, VISTA-TV, NoTube (as coordinator), Open PHACTS, Big Data Europe and EuropeanaConnect. The group has specific expertise in applying its research to applications in the cultural heritage, media and broadcasting domains through close collaborations with institutions such as the Rijksmuseum, BBC, IBM and Europeana. The group specializes in human-computer interaction, human computation, collective intelligence, crowdsourcing, semantic interoperability, semantic annotation, niche sourcing, recommendation systems, and semantic search facilities for distributed data.