DFG Research Training Group "Computational Systems Biology"
Biology has developed into a quantitative, information- driven science. We are seeking doctoral students for the new Research Training Group "Computational Systems Biology" (CSB) which focuses on the system-level understanding of biological data using computational methods. CSB is embedded in the thriving Berlin life-science environment and involves high-ranking research institutions such as Humboldt-Universitä t zu Berlin, Freie Universität Berlin, Charité-Universitä tsmedizin Berlin, Max Planck Institute for Molecular Genetics and Max Delbrück Center for Molecular Medicine Berlin-Buch.
We offer 3 PhD fellowships for the following projects (supervised by the specified faculty members):
1. Characterization of the differential protein-mRNA interactome during C.elegans development (Rajewsky/Landthale r)
2. Characterization of protein-RNA interactions based on next-generation sequencing data (Landthaler/ Rajewsky)
3. Modelling and analysis of lipid droplet metabolism (Holzhütter)
We are looking for highly motivated students with a strong interest in systems biology and mathematical modelling. Successful applicants must hold a master's level degree in life sciences, mathematics, physics or computer sciences. The PhD fellowship comprises a monthly stipend which is initially granted for 2 years and which can be extended for another 12 months. We offer an excellent training program in computational systems biology, supervision by at least two experienced scientists, and multifarious opportunities for developing local and international collaboration.
For online application see here. Application deadline is 23 December, 2011.
Contact:
GRK Computational Systems Biology
c/o Dr. Cordelia Arndt-Sullivan,
Humboldt-Universitä t zu Berlin
Institut für Biologie
Theoretische Biophysik
Invalidenstr. 42
10115 Berlin
Germany
email: info@berlin- csb.de
Humboldt-Universitä t zu Berlin is an equal opportunities employer.
url: http://www.berlin-csb.de/index.php?option=com_content&view=article&id=41:kurzmeldung-5&catid=3:kurzmeldungen
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