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Maastricht University is renowned for its unique, innovative, problem-based learning system, which is characterized by a small-scale and student-oriented approach. Research at UM is characterized by a multidisciplinary and thematic approach, and is concentrated in research institutes and schools. Maastricht University has around 15,000 students and 4,000 employees. Reflecting the university's strong international profile, a fair amount of both students and staff are from abroad. The university hosts 6 faculties: Faculty of Health, Medicine and Life Sciences, Faculty of Law, School of Business and Economics, Faculty of Humanities and Sciences, Faculty of Arts and Social Sciences, Faculty of Psychology and Neuroscience.
New nanomaterials increasingly find their way to the European market, because of their unique applications. Understanding the (lack of) safety of nanomaterials is therefore of utmost importance. This postdoc position takes responsibility for the development of new intelligent methods that strengthen our understanding of the safety issues of nanomaterials. The project will explore and contribute to the required bioinformatics and statistical solutions in order to develop predictive models of safety-related properties, where main focuses will be the development of ontologies, Linked Data approaches, and cheminformatics and chemometrics methods. This work is conducted as part of an EU FP7 project serving the European NanoSafety Cluster (NSC, www.nanosafetycluster.eu/ ) community. You will be primarily responsible to meet our group's deliverables and to actively contribute to the FP7 project and the NSC community. However, the project also provides the opportunity to continue promising research in development and use these state-of-the-art technologies to explain the biological and chemical mechanisms and pathways involved in the safety of the various types of nanomaterials.
We seek an all-round, experienced post-doc with excellent academic abilities and a proven track record in participating in international projects. You have completed a PhD in the natural sciences, such as biology, chemistry, chemical engineering, or equivalent, and preferably one or more years of post-doc experience. A paper track record showing experience with applying programming and multivariate statistics to solve biological and chemical problems is a must. We are looking for a prospective post-doc with (in random order):
A background in material science, toxicology, or nanotechnology, but also with affinity for data integration issues (e.g. ontologies) and quantitative methods (e.g. regression methods);
Good organisational skills and experience with participating and cooperating within international projects;