We are looking to hire two researchers with a background in machine learning, bioinformatics of genomic and molecular data, and/or statistical genetics. Strong skills in computer programming and modeling are necessary. Researchers with experience in human, animal, and other model organisms and evolution are especially encouraged to apply.
Join Ed Buckler and his group at Cornell University and help develop models based in genomics, molecular biology, and large-scale field evaluations to predict fitness consequences of mutations. We have a history of bridging quantitative genetics and genomics and applying this knowledge to improving crops with the aim of reducing hunger and environmental impact. Crop genomics is at a key moment, where the technologies to design crops (genomic selection and genome editing) have never been more powerful. However, to effectively apply these technologies, we must first improve our capacity to integrate functional variants into their regulatory and physiological networks. We are looking for researchers wanting to tackle these important problems of modeling chromatin, expression, translation, protein-protein interactions, and enzyme activity in order to connect these molecular changes to a wide range of phenotypes.
Specifically, we are building tools to develop plant regulatory networks that leverage data from models such as Arabidopsis, yeast, and Chlamydomonas, and apply the networks initially to maize, sorghum, and cassava. Longer term these networks will be tested in hundreds of related wild species.
The three specific efforts we are focused on are: (1) Apply and develop machine learning tools to catalog protein-protein, protein-nucleic acid, and protein-metabolite interactions to identify likely causal networks. (2) Model the effects of deleterious mutations through causal networks in maize, sorghum, and cassava. (3) Model the effects of temperature and moisture adaptive variants through causal networks in maize, sorghum, and a thousand related species.
There are two positions available. This is a one-year term appointment with the possibility of extension contingent upon continued funding and successful performance.
Interested candidates should submit a cover letter and CV to Sara Miller at firstname.lastname@example.org.
Ph.D. in Biology, Bioinformatics, Computer Science, Genetics, or related field.
Background in machine learning, bioinformatics of genomic and molecular data, and/or statistical genetics.
Strong skills in computer programming and modeling are necessary.
Researchers with experience in human, animal, and other model organisms and evolution are especially encouraged to apply.
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Internal Number: JOB_POSTING-3-35315
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