Postdoc in Computational Biology
Postdoc in Computational Biology
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Job Description
The VIBUGent Center for Plant Systems Biology (PSB ) is a worldleading plant science institute with the mission to unravel the biology of plants and use the insights gained to improve the sustainability of agriculture and the climate change resilience of crops. The Maere lab at PSB () is active in the fields of computational biology evolutionary genomics and plant systems biology. Current research topics include testing a novel experimental setup to unravel the molecular wiring of plant phenotypes under field conditions studying dosage balancesensitive genes in plants and modeling the evolution of transcriptional systems in silico. We are currently looking for a talented postdoc to join our team in the context of the VIB Grand Challenge project Validating the use of endemic rhizobia for sustainable soybean cultivation in Northwestern Europe.
Project description
Soybean (Glycine max) is one of the most important protein sources in human food and animal feed. Europe is highly dependent on the import of soybean often from (sub)tropical areas where soybean cultivation is one of the main drivers of deforestation. Elishing European soybean production at scale may not only help curtail deforestation in crucial ecosystems such as the Amazon Cerrado and Gran Chaco but would also reduce Europes dependence on soybean imports reduce the ociated carbon footprint and facilitate the ongoing transition to more plant protein and less animal protein in the European diets. Moreover in symbiosis with nitrogen (N)fixing bacteria (rhizobia) inside root nodules soybean can manage its own N nutrition reducing the need for N fertilization. In combination with a reduced import of soybean products (and the ociated N) from overseas local and circular soybean cultivation may help mitigating some of the N pollution issues in Europe. Although soybean is predominantly a (sub)tropical crop soybean varieties that are better adapted to more temperate climates with cold springs such as those found in Northwestern (NW) Europe have already been bred. However the current commercially available rhizobial inoculants needed to elish Nfixing nodules on soybean roots are based on nonendemic rhizobial strains that are poorly adapted to local environmental conditions causing soybeans cultivated in NW Europe to have suboptimal and variable yield and protein content. The aim of this project is to ess whether using endemic rhizobia as soybean inoculants instead of exotic strains improves soybean yield protein content and yield ility.
Profile
- You have a PhD in Computational Biology Biotechnology or Bioengineering.
- You have excellent project management and communication ss and you like to take initiative.
- You have solid expertise in machine learning ysis of genomics data and data integration. Handson experience with microbiome data ysis is a plus.
- You are fluent in English (spoken and written) and master several programming languages.
- You found a good balance between working in a team and working independently.
- You are meticulous wellorganized responsible and selfcritical.
- You have a pion for science and you can think outside the box.
We offer
- A 3year postdoc position with an attractive salary.
- An exciting work environment in a top research institute.
- The opportunity to be part of a dynamic interdisciplinary and international team.
- A challenging project with high societal value.
- Ample opportunity to learn new ss.
How to apply
Please complete the online application procedure and include a detailed CV a letter of motivation and the contact details of two referees. Applications are accepted until the position is filled. For more information contact Steven Maere ().
Employment Type
Full Time
