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backgrounds. The Faculty external link has six departments: Biology, Pharmaceutical Sciences, Information & Computing Sciences, Physics, Chemistry and Mathematics. Together, we external link work on excellent
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for growth and development, with a strong internal recruitment policy and excellent training programmes. You will work on the greenest and most innovative campus in the Netherlands, in an international and
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fulltime and starts with a temporary employment contract for 18 months. This probationary period consists of an educational programme, offered by Erasmus Graduate School of Law, and individual research and
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internationally-oriented, interdisciplinary research of high societal relevance with our research programme TRACE (Transformations, Communities, and Environments). Our motto is: we are making places better
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of the Belastingdienst (Dutch tax agency), and with our Dual Career Programme we will also help your partner find the right job for them. Employer Erasmus University Rotterdam (EUR) is an internationally oriented
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you come with a partner, we offer a Dual Career group program organized by our Welcome Center . This program helps your partner to get familiar with the Dutch culture, labour market and assists your
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. The Faculty offers a wide range of degree programmes: 15 Bachelor's programmes and over 35 Master's specialisations. Our research, which is internationally widely acclaimed, covers Archaeology, Cultural
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cultural activities at Radboud University as an employee. And, of course, we offer a good pension plan. We also give you plenty of room and responsibility to develop your talents and realise your ambitions
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for yourself and, if applicable, for your family. If you come with a partner, we offer a Dual Career group program organized by our Welcome Center . This program helps your partner to get familiar with the Dutch
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large-scale neural models of the early visual system. Requirements The successful applicants will have: A solid computational background, an interest in cognitive neuroscience and strong deep learning