The Dynamic and Distributed Information Systems Group at the University of Zurich (Switzerland) is inviting applications for
multiple PhD student positions
in the area of Big Data Processing with a keen interest in at least one of the following areas:
- Petabyte Scale Stream Processing
- Privacy Preserving Big Data Processing
- Heterogenous Data Integration
To work in two recently granted projects funded by the Swiss National Science Foundation (see project descriptions below).
The PhD students will be supervised by Prof. Abraham Bernstein and, for some positions, co-supervised by Prof. Michael Böhlenof the Database Technology Group. The focus of the research activities will be in the broad areas of Big Data Processing of large data streams and heterogeneous data processing and will be linked with a number of new and existing research projects (including ViSTA-TV, CrowdLang, Signal/Collect). Most projects in the group are funded by research foundations such as the Swiss National Science Foundation, the EC, or the HaslerStiftung.
- a team of young and highly motivated colleagues, who are passionate about research
- a work environment that is well equipped with the newest hardware and software technology
- a competitive salary (annually about 55'000-60'000CHF/~55',000€ for PhD students; 80'000-90'000CHF/~81'000€ for postdocs)
- strong support for your personal development and career planning
- an attractive work environment both within the research group and beyond: the University of Zurich is one of Switzerland's leading universities in the middle of a vibrant, cosmopolitan city that regularly ranks as one of the cities with the highest quality of life in the world
- A highly successful PhD program with graduates at top rated institutions world-wide
- a master's degree in informatics, computer science, information systems (or an equivalent university study) for the PhD student position and a PhD degree for the postdoc position.
- strong analytical/mathematical skills
- expertise in database systems, Semantic Web / Linked Data, HCI, or economics is helpful.
- good programming skills in several languages
- an interest in applying computer science research to real-world problems
- excellent command of English; German is a plus but not required
If you fit the profile, are able to work in a team, like challenging tasks, and are passionate about research then we would love to hear from you. To apply for one of the positions, please send a detailed curriculum vitae, all grade transcripts, selected publications (if available), a list of at least three references, and your BSc/MSc theses in one PDF file by email to
Prof. Abraham Bernstein, Ph.D.
Department of Informatics
University of Zurich, Switzerland
Email: ddisjobs "at" lists "dot" ifi "dot" uzh "dot" ch
Additionally, applicants may arrange for three letters of recommendation to be sent to Prof. Bernstein directly, however, this is not required.
We will continuously review applications until Jaunuary 8th, 2017.
The University of Zurich is committed to enhancing the number of women in scientific positions and, therefore, particularly invites women to apply. Women who are as qualified for the position in question as male applicants will be given priority.
According to the Economist big data production will soon outpace available storage and the availability of computer science experts who know how to handle such data. At the same time, society is increasingly concerned about data protection. To address this gap, we need stream processing systems that continuously analyze incoming data rather than store it and allow domain experts to specify its analysis in a privacy preserving manner. The project will focus on the development of a peta-scale stream analytics system that enables domain experts to analyze high-performance data streams whilst running on a commodity cluster. The solution will support real-time advanced linear algebra operations while ensuring the privacy of the data. If successful, this project will vastly simplify the development of new, societally acceptable applications leveraging real-time data analytics.
PIG DATA: Health Analytics for the Swiss Swine Industry
In pig farming as in most aspects of animal husbandry, methods for the processing of big data have not yet been introduced. The pig farming industry has an extremely complex, small-scale structure. It consists of a large number of smaller, highly networked producers at different stages of the production process. At all these stages, large amounts of data are being accumulated. Adequate handling and evaluation of this information is necessary to identify previously unknown interrelationships, causes and risk factors in order to identify the best strategies to combat them. The goal of this project is to provide methods to better understand and optimize the structure and complexity of pig farming in Switzerland in order to improve animal health and well-being.