The Informatics Institute, one of the research institutes within the Faculty of Science, and the Leibniz Center for Law, one of the research institutes within the Faculty of Law, have a vacancy for a Postdoctoral researcher on Simulation Environment.

While 100% data quality is the imposed target level, it is recognized that this is an unrealistic number from a practical perspective. Missing from a data analytics perspective, are scientific tools that treat residual data failures and mistakes as systematic uncertainties, and that propagate these systematic effects in the predictions coming from the models. In other words tools that are able to quantify the accuracy and reliability of the predictions of models and algorithms under the assumption that the data is not 100% correct.

The postdoctoral researcher will contribute to a simulation framework that allows simulation of (historic) data and systematic effects to test the robustness and reliability of algorithms and models. The framework should be able to autonomously identify and extract relevant correlations from any given dataset and simulate a dataset that reproduces these correlations. Furthermore, the framework should be able to generate distortions to these (simulated) datasets that reflect in various levels the impact of known and measured data quality issues. The distorted datasets are fed into risk models to study the robustness of an algorithm or model and to assess the impact of (systematic) uncertainties. It should also be possible to simulate alternative scenarios, i.e. (business) policies or decisions, within this framework. The goal being to identify the optimal scenario according to an arbitrary set of criteria from e.g. an economical or societal point of view.

Candidates should have completed a PhD in artificial intelligence, computer science or a related field, or will do so on short term. The candidates must have knowledge of data science, statistics, model building, mathematics and strong coding abilities. Knowledge of representation of norms and normative reasoning is considered as a pre. The candidates must have the ability to communicate their ideas and results to scientific and business audiences. Their grades demonstrate their knowledge and ambition, their PhD thesis their ability to independently contribute to research in Computer Science and/or Artificial Intelligence.

The appointment will be full-time (38 hours a week) on a temporary basis. Initial appointment will be 18 months. Periodic evaluation will be held and upon positive evaluation, the appointment will be extended to a total of 48 months.

The gross monthly salary will be in accordance with the university regulations for academic personnel, and will range from €2,588 up to a maximum of €4,757 (salary scale 10/11) based on a full-time appointment depending on qualifications, expertise and on the number of years of professional experience. There are also secondary benefits, such as 8% holiday allowance per year and the end of year allowance of 8.3%. The Collective Labour Agreement for Dutch Universities is applicable.

Among other things, we offer:

• competitive pay and good benefits;
• top-50 University worldwide;
• one of the best deep learning ecosystems in the world;
• interactive, open-minded and a very international city;
• excellent computing facilities.

English is the working language in the Informatics Institute. As in Amsterdam almost everybody speaks and understands English, candidates need not be afraid of the language barrier.

University of Amsterdam

With over 5,000 employees, 30,000 students and a budget of more than 600 million euros, the University of Amsterdam (UvA) is an intellectual hub within the Netherlands. Teaching and research at the UvA are conducted within seven faculties: Humanities, Social and Behavioural Sciences, Economics and Business, Law, Science, Medicine and Dentistry. Housed on four city campuses in or near the heart of Amsterdam, where disciplines come together and interact, the faculties have close links with thousands of researchers and hundreds of institutions at home and abroad.  

The UvA’s students and employees are independent thinkers, competent rebels who dare to question dogmas and aren’t satisfied with easy answers and standard solutions. To work at the UvA is to work in an independent, creative, innovative and international climate characterised by an open atmosphere and a genuine engagement with the city of Amsterdam and society.

http://www.uva.nl/en/home

Faculty of Science and Faculty of Law

The Informatics Institute is one of the research institutes within the Faculty of Science. The Leibniz Center for Law is one of the research institutes within the Faculty of Law. Both institutes are running broad research programs with an overlapping interest in simulation based policy analysis.

Data from different organizations could provide significantly more value when combined and processed using algorithms from yet other organizations. However, data are frequently too large, sensitive or valuable to travel outside the home organisation. Federated data science solutions address this problem by either collecting the data at secure data hubs that enforce access control, protection and auditing or bringing algorithms to the data and applying distributed learning. These solutions should respect the FAIR (Findable, Accessible, Interoperable, Reusable) principles.

The VWData project develops a proof-of-principle architecture for a network of FAIR secure data-hubs and services, combining federated data science methods with fully auditable processes. The developed services aim at offering non-trivial learning and reasoning on the data with full transparency. Moreover, the project will deliver a quantitative assessment of the reliability and integrity of the data and results given different types of data quality and heterogeneity of inputs. The proof-of-principle will be demonstrated by a health use case (UL/TNO), but can also be applied in Astronomy (VU, ASTRON, NLR), Logistics, Smart-Industry (UvA/UL/Air France KLM). The health case is based on H2020 project RECAP coordinated by TNO, where 20 national cohort non-uniform data sets need to be analysed.