Prof. Peter van Tienderen is dean of the Faculty of Science as of 1 January 2017. He had been vice-dean of the Faculty since 2014.
Peter H. van Tienderen (1958) studied biology at the University of Utrecht with ecology & evolution and informatics as subject areas. He did his PhD work at the Netherlands Institute for Ecology (NIOO) and defended his thesis in 1989 at Utrecht University. He worked as postdoc at Duke University, before accepting a tenured position at the Wageningen UR, and later a dual research position at Wageningen/NIOO.
In 2001 he became Professor of Experimental Plant Systematics at the University of Amsterdam, and was director of the Institute for Biodiversity and Ecosystem Dynamics (IBED) from 2005-2014.
The group Experimental Plant Systematics studies the origin and maintenance of genetic variation in plants, its relationship with the plant's breeding system, and its role in the process of adaptation. Van Tienderen’s own research interests focus on adaptation and differentiation in higher plant species, in relation to their breeding system. He published over 60 papers in peer reviewed journals, recent work addressing the consequences of the introduction of genetically modified (GM) crops, and the origins of functional biodiversity.
Van Tienderen has lectured in many courses at all levels, from the Plant Biology course for first year Biology Bachelor’s students to advanced courses for PhD students. He helped with the start of the highly successful interdisciplinary Bachelor’s programme Future Planet Studies, which was initiated by Prof. Willem Bouten. In this programme he contributes as lecturer to the courses Future of the Earth (1st year) and Food production (2nd year).
In 2010 he became the project leader for LifeWatch in the Netherlands, an ESFRI Landmark project and the European research infrastructure for biodiversity and ecosystem research. Modern research relies heavily on the analysis of complex and heterogeneous data, coming from, for instance, high throughput genetic analyses, sensor networks, remote satellite sensing, as well as user communities (‘citizen science’). This means that this research depends on the use of modern eScience methods for data acquisition, data mining, visualisation, analysis and modeling.