Postdoctoral Associate - Section of Plant Breeding and Genetics

Position Summary:

We seek a postdoctoral fellow with a strong background in plant science, biostatistics and quantitative genetics to join a research project studying iron toxicity tolerance in rice. The position is funded as part of an international initiative funded by a grant from the Bill & Melinda Gates Foundation. The successful candidate will work with a team of biologists, breeders and software engineers to:

Undertake a Genome Wide Association Study (GWAS) to identify QTLs associated with plant response to iron toxicity measured in multiple years and field locations in West Africa; Optimize the GWAS model to extract maximum signal from noisy data; i.e., fit multiple markers, multiple traits, multiple environments, GXE terms; Integrate prior information to improve model performance (i.e., candidate genes, QTL identified in other studies, ontologies, expression networks, etc.); Identify candidate
genes informed by physiological studies, implement validation experiments, prioritize QTL for breeding applications.

Additional responsibilities include (a) maintain regular communication with project members, including national and international collaborators, (b) provide training and support for GWAS and QTL analysis, as needed, (c) prepare manuscripts for publication, work plans, project reports, and
presentations, (d) occasionally travel to meetings.

The postdoc at Cornell will be expected to stay abreast of emerging technologies that increase the throughput, decrease the cost, and improve the efficiency of genomic-assisted breeding applications.

Required Qualifications:

Ph.D. in Plant Breeding, Plant Science, Quantitative Genetics, Statistics or related field; experience in multi-investigator research environment; excellent communication and interpersonal skills; familiarity with biological data-mining and genomic information management; fluent spoken and written English; demonstrated ability to write and publish scientific papers in refereed journals. International research experience preferred.

Preferred Qualifications:

Basic programming skills (R, Python); experience working with large data files; familiarity with Bayesian statistics and genomic analysis; interest in international agriculture.

How to Apply:

Please send your CV and application materials to Professor Susan McCouch, srm4@cornell.edu


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