The Department of Molecular Biology and Genetics at Aarhus University, Denmark is seeking a creative, talented and motivated Postdoctoral Fellow with experience in epigenetics and statistical genomics in plants. This position represents an exciting and unique opportunity to design and implement cutting edge statistical analysis in crops, and to publish high-impact papers. The position is open for 36 months and the expected starting date is as soon as possible.
The post doc position is linked to the RadiMax project (http://mbg.au.dk/en/news-and-events/news-item/artikel/dybe-roedder-er-roden-til-alt-godt-1/) and aimed at developing deep-rooted crops with improved drought tolerance. Large-scale above- and below-ground phenotyping for drought response will be performed in the recently constructed RadiMax root phenomics facility, which is a unique high-throughput root phenotyping facility that exploits recent advances in biological image analysis and mathematical modelling.
The focus of this project is to develop and validate sophisticated statistical methods and prediction models that efficiently use information from multi-omics data (genomes, transcriptomes and epigenomes) to discover systems level insights into the genetic architecture of drought response in crop plants. The main focus will be on epigenetics analysis in relation to abiotic stress response in crops.
The ideal candidate will have an interdisciplinary training with a strong epigenetics and statistical genetics background, knowledge about plant breeding, and experience with integration of high-dimensional genomic and phenotypic datasets, to discover systems level insights into complex trait architecture in crop plants.
The successful candidate will be part of a strong research program, which aims to implement genomic selection in commercial breeding programs, and bring predictive power to our understanding of complex traits in plants.
Qualifications and competences
• PhD or equivalent in statistical genetics or epigenetics
• Experience in multi-omics data integration and analysis
• Ability to work in collaborative partnership networks with external academic and industry partners
• Proficiency in high-performance computing
• Advanced competences in complex trait analysis in plants
• Excellent written and verbal communication skills in English, and the ability to explain complex data to non-experts
• Knowledge of a high level programming language like C or C++ will be an advantage
• Strong coding skills (Python, Java, R)
• Strong understanding of the fundamentals of statistical models and prediction
• Strong statistical and quantitative skills and familiarity with statistical modeling and software
Only candidates with strong statistical genetics skills, ability to work with large datasets and as part of a team, and excellent communication skills will be considered.
Place of work
The Section for Crop Genetics and Biotechnology (CGB) is part of the Department of Molecular Biology and Genetics and is located at the AU Flakkebjerg campus. The place of work is Forsøgsvej 1, 4200 Slagelse and the area of employment is Aarhus University with related departments.
The section provides a very dynamic and internationally orientated environment with strong collaborations with relevant industrial partners and with leading international research groups in the area. The working language is English.
CGB is interested in the analysis of complex traits in plants using multi-omics data integration and analysis using systems genomics approaches and environmental information. CGB is currently deeply involved in strategic research concerning the use of genomic information for prediction of phenotype and genotype, and to develop efficient methods for using large scale and dense genotype information in prediction.
CGB possesses state-of-the-art equipment, a high-performance computing cluster, and facilities for undertaking the relevant research.
For further information please contact Senior scientist Torben Asp, +45 8715 8243, e-mail: (Torben.email@example.com)
Alle ansøgninger skal være lavet online og modtaget senest:15.01.2017
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