丹麦Slagelse植物统计遗传学博后研究员

The Department of Molecular Biology and Genetics at Aarhus University, Denmark is seeking a creative, talented and motivated Postdoctoral Fellow with experience in statistical genetics 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. A key impediment to genetic analysis of root architecture in crops is the ability to perform accurate and large-scale root phenotyping. This 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 root development in crop plants.

The ideal candidate will have an interdisciplinary training with a strong 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. We are seeking applications from dynamic, motivated individuals who will play a key role in solidifying the department’s research in this area.

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.

We are seeking applications from dynamic and motivated individuals with several years of experience in statistical genetics, who will be expected to play a key role in solidifying and further expanding the research in this area.

Qualifications and competences

• PhD or equivalent in statistical genetics in animals or plants
• 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 competences, 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.