Open: PhD student or PostDoc position in bioinformatics for marine prospecting
Despite ongoing efforts to increase the percentage of renewable energy resources, hydrocarbons (HC), i.e. oil and gas will remain an important energy source for the next several decades.
One of the key factors to reach EU’s ambitious goals in reduction of greenhouse gas emissions is the replacement of coal by alternative energy sources, mainly natural gas. Utilizing EU-borne energy resources is a key factor for reaching these goals. Prospection of fossil fuels in densely populated areas like Europe is bound to tight environmental regulations and comes at economic costs. The need to decarbonize our energy system and promote the sustainability of European economies leads to conventional hydrocarbon prospection facing public resistance. Finding a balance between energy demands and responsible management of environmental resources remains a challenge in the years ahead. The EU project PROSPECTOMICS is going to provide a possible solution by deploying a radically new approach based on biomolecules like DNA, RNA and proteins related to hydrocarbon-degrading microorganisms in marine sediments.
CUBE, the Division of Computational Systems Biology, is a group of bioinformaticians and computational biologists. We are interested in understanding of biological systems, ranging from single species to multi-species systems and ecosystems, based on data from large-scale bioanalytical methods. We develop, improve and apply computational methods for the interpretation of molecular information in biology, and establishes and analyses quantitative mathematical models. Within the EU project PROSPECTOMICS, we will analyse and model the experimentally generated data. We will use recent bioinformatic methods to assemble, bin and interpret metagenomic data, with specific focus on microbial degradation. We will statistically and functionally analyse metatranscriptomic and metaproteomic data. We will integrate all data with statistical models and will develop predictive models by using latest machine learning approaches.
If you are interested in joining this project as PhD student or PostDoc, we look forward to your application. Please send your CV, motivation letter and scientific references to
