(1) Microbial-based predictive modeling of agro-ecosystems: Crop productivity results from a complex interplay between environmental biotic and abiotic factors, including N availability. The fragile equilibrium between N-cycling, transport and crop yields is mostly due to soil microorganisms that control biogeochemical cycles. Recent advances in high-throughput sequencing technologies now allow to link soil biotic and abiotic properties with the metabolic activities of soil microorganisms. By defining the links between crop yields, soil abiotic conditions and soil microbial taxonomic and metabolic profiles, we hope, in the longer term, to create novel predictive tools with far better resolution than current routine agronomical practices that are limited to soil nutrient analyses
(2) Multifactorial agronomic decision tool to design biologically optimal cover crops: It has been suggested that more complex agro-ecosystems that more closely mimic diverse natural ecosystems are more resilient and environmentally sustainable. One key management strategy to potentially cope with impending climatic change is to increase agro-ecosystem diversity, using more complex crop rotations and incorporating winter cover crops (WCC). Manipulation of the environment to drive microbial communities into ecosystem services is an exciting challenge in sustainable management of agroecosystems. The current project will add value to several long-term field studies that focused on evaluating the impact of management practices on key soil ecosystem services, including greenhouse gas emissions, soil microbial diversity, soil organic matter, biogeochemical cycling, soil structure and plant productivity, by adding metagenomic analysis of soil microbial communities to archived DNA and RNA samples. The overarching outcome of this research project is to develop a multifactorial agronomic decision tool to guide WCC selection for improved soil health and microbial biodiversity and crop productivity.
In both projects, next-generation sequencing will be used in conjugation with other more traditional soil microbiological and plant techniques. Experiments will be carried out in the field, greenhouses or in growth chambers.
Requirements: A B.Sc, MSc or equivalent in biology, microbiology, ecology, statistics, informatics, mathematics or bioinformatics is required. Candidates should have experience or a strong interest in large-scale data analysis, statistics bioinformatics or modeling. We are looking for a creative, highly motivated, autonomous scientist with excellent organizational and communication skills and excellent attention to detail.
Duties and responsibilities: Under the guidance of Dr. Philippe Constant and Dr. Étienne Yergeau, the students will be responsible for experimental planning and set-up, data generation, analysis, management and synthesis, as well as manuscript preparation. Students will work in close collaboration with other students and post-docs on the project.
Appointment and salary: The positions are for 2 (MSc) or 3 years (PhD). Salary will be of 16,000 to 18,000 CAN$ per year. Expected starting date is September 1st, 2018 or later (to be discussed).
Location: Centre INRS - Institut Armand-Frappier, Laval, QC, Canada
Additional information: For additional information, please contact Dr. Étienne Yergeau (Etienne.Yergeau@iaf.inrs.ca) of Dr. Philippe Constant (Philippe.Constant@iaf.inrs.ca).
How to apply: Application will be accepted until the positions are filled. Interested persons are requested to submit a complete CV, a statement of the candidate's interest and suitability for the position and contact information for three references by email to Etienne.Yergeau@iaf.inrs.ca.