PhD position on AI and Data Engineering for Privacy
The research will be conducted under supervision of prof. W.J. van den Heuvel en co-promotor dr. D. Tamburri. The successful candidate is expected to: Perform …
- Sint Janssingel, Den Bosch, Noord-Brabant
- Tijdelijk contract / Tijdelijke opdracht
- Uren per week:
- 40 - 40 uur
The research will be conducted under supervision of prof. W.J. van den Heuvel en co-promotor dr. D. Tamburri.
The successful candidate is expected to:
- Perform scientific research in the domain described;
- Present results at (international) conferences;
- Publish results in scientific journals;
- Participate in activities of the group, mainly in 's-Hertogenbosch but sometimes at KPN the commercial partner.
- Have a MSc. in Mathematics, Statistics, Computer Science, Econometrics, AI or a related discipline;
- Have a strong interest in Data Engineering and Artificial Intelligence;
- Have excellent analytical skills is highly motivated and rigorous;
- Have good technical understanding of models used in data engineering/science;
- Have knowledge of, or a willingness to familiarize themselves with, current research into new and innovative data science techniques;
- Be a fast learner, autonomous and creative, show dedication and be hard working;
- Possess good communication skills and be an efficient team worker;
- Be fluent in English, both spoken and written.
The PhD student will be employed at Tilburg University.
- A full-time position.
- The selected candidate will start with a contract for one year, concluded by an evaluation after approximately 10 months. Upon a positive outcome of the first-year evaluation, the candidate will be offered an employment contract for the remaining three years.
- A minimum gross salary of € 2,325 per month up to a maximum of € 2,972, in the fourth year;
- A holiday allowance of 8% and an end-of-year bonus of 8.3% (annually);
- Researchers from outside the Netherlands may qualify for a tax-free allowance equal to 30% of their taxable salary (the 30% tax regulation). The University will apply for such an allowance on their behalf;
- Assistance in finding accommodation (for foreign employees);
- The opportunity to perform cutting edge research in a large-scale joint data science project involving TiU, TU/e, JADS and a commercial partner and bringing together expertise of several senior researchers;
- Support for your personal development and career planning including participation in courses, summer schools, conference visits, research visits to other institutes (both academic and industrial), etc.;
- A broad package of fringe benefits (including excellent technical infrastructure, savings schemes and excellent sport facilities).
Privacy by design is a major challenge for the telecommunication industry. The goal of the project is to realize privacy by design by means of AI for the telecommunications domain. Currently, KPN has access to valuable data sources that cannot be used because of privacy requirements. In the project AI-based anonymization methods will be developed that remove any privacy-sensitive information.
Furthermore, KPN has an unknown number of contextual conditions and constraints that need to be taken into consideration when anonymizing the data, not only restricted to the General Data Protection Regulation (GDPR) and Authority Privacy (AP) in the Netherlands. Since KPN has millions of customers and many potential events to use, scalability of data anonymization becomes extremely challenging due to the dataset size and data complexity. It is key for KPN insight- and value-generating activities that KPN investigates anonymization techniques which are (1) as generic as possible and (2) as applicable as possible for multiple use cases under the above restrictions. To provide feasibility analysis, the research in question will start from experimental techniques in more limited scenarios, e.g., chat data.
KPN is one of the forefront runners in following the privacy-by-design paradigm. However, there are a number of use cases where the KPN private-by-design processes can be improved in a data-driven, machine-supported, and experiment-based fashion. Currently, lack of proper anonymization prevents KPN from executing these use cases.
More extended project information can be provided on request. Please contact prof. W. J. van den Heuvel.