Cornell University, School of Integrative Plant Science – Section of Plant Breeding and Genetics

Position ID:Cornell-School of Integrative Plant Science – Section of Plant Breeding and Genetics-POSTDOC [#15773, WDR-00021917]
Position Title: Machine Learning Genetics of Oat Composition
Position Type:Postdoctoral
Position Location:Ithaca, New York 14853, United States [map]
Subject Areas: Computer Science / Machine learning methodology and applications
Genetics / Functional Genetics
Agriculture / Oat
Appl Deadline:2020/03/15help popup (posted 2019/12/18, listed until 2020/03/15)
Position Description:    

*** the list date or deadline for this position has passed, and no new applications will be accepted. ***

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Postdoctoral Associate
Machine Learning Genetics of Oat Composition
School of Integrative Plant Science
Cornell University



The position is in the Plant Breeding and Genetics Section at Cornell University, and is part of a USDA Agriculture and Food Research Initiative grant to breed more nutritious oat. Oat is uniquely valued among grain crops for the health-promoting composition of its seeds. This position seeks to apply novel machine learning methods to extensive genomic, transcriptomic, and metabolomic datasets. Results should generate effective methods to improve the composition of oat seed. 

The Plant Breeding & Genetics Section, within the School of Integrative Plant Science, trains interdisciplinary scientists in the elaboration of new breeding methods, the discovery of genetic mechanisms important for economically important traits, and the creation of genetic stocks, germplasm, and varieties. We promote a collaborative and interactive workspace to improve learning, cross connectivity, and mutual support between basic and applied researchers. Cornell University plant breeders are world leaders in innovative plant breeding research, teaching, and extension, and we collaborate globally.

The Jannink lab works with several crop species (wheat, oat, barley, cassava, and the brown algae sugar kelp) to develop genomic prediction methods and integrate them optimally into breeding schemes. We work together to discover, build on, and share new ideas and tools from across computational disciplines that lead to successful applied breeding outcomes. With the Jannink lab, Dr. Michael Gore and Dr. Mark Sorrells provide leadership on the multiomic oat selection project.

In research for this project, the postdoc will collaborate with oat breeders at Universities in Minnesota, Wisconsin and South Dakota, as well as a postdoctoral associate currently working on the project. We have characterized an oat diversity panel of 384 genotypes with high-density DNA marker data, RNA-seq gene expression data, and non-targeted LC-MS, GC-MS, and targeted fatty acid methyl ester data of mature oat seed. We will analyze these data to identify important genomic drivers of the mature oat seed metabolome. We will test whether results from this analysis can improve prediction accuracy in a series of 18 biparental crosses. We will also sequence a population of 1,500 oat TILLING (Targeted Induced Local Lesions In Genomes) lines at putative causal loci to determine if their metabolomes are indeed affected.

We seek a candidate with machine learning expertise, specifically deep learning neural network methods, and interest in applications relating to genetic variation and complex biological systems. The datasets described have rich structure and are amenable to machine learning analyses to identify patterns eluding linear models. Aided by other project personnel, the postdoc will identify patterns in the metabolomic and transcriptomic assays and their associations with genetic variation. The postdoc will construct learning models to identify multifactorial causes of seed composition variation, enabling the prediction of the impact of genetic perturbation on seed composition and suggesting effective breeding strategies to leverage this ability.

Anticipated Division of Time
Field work, sample prep, data collection - 25%
Machine learning analysis and interpretation - 30%
Writing - 30%
Training of lab members and collaborators in machine learning - 15%

Position Requirements
Ph.D. in engineering, statistics, or computer science focused on machine learning, with experience or interest in genetic or complex biology applications, or Ph.D. in genetics with emphasis on machine learning analysis of genetic data. Proven scientific writing ability and communication skills.

Preferred Specific Skills
Knowledge of genetics and genetic data types and analysis. Knowledge of biochemistry, metabolomics or systems biology methods. Basic bioinformatics skills (sequence alignment, use of gene annotations). Basic notions of plant or animal breeding.

How to Apply
Candidates should send a statement of interest, curriculum vitae, contact information for three references and a statement of diversity, equity and inclusion.  Submit all application materials to Academic Jobs OnlineQuestions about the position can be addressed to Dr. Michael Gore at: mag87@cornell.edu . Review of applications will begin immediately and continue until the position is filled.


Diversity and Inclusion are a part of Cornell University’s heritage. We are a recognized employer and educator valuing AA/EEO, Protected Veterans, and Individuals with Disabilities.

Application Materials Required:
Submit the following items online at this website to complete your application:
And anything else requested in the position description.

Further Info:
www.cornell.edu
email
607/255-5492
 
School of Integrative Plant Science
Section of Plant Breeding and Genetics
Cornell University
Plant Science Building, Room 358
Ithaca, NY 14853

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