The post-doctoral scientist will carry out research for the Great Lakes Bioenergy Research Center (GLBRC), with the duty to identify the genetic basis of the bioenergy crop switchgrass' traits that are relevant for its productivity in marginal lands. The employee will use machine learning computational biology to carry out this research and works alongside other postdocs as well as undergraduate and graduate students and other scientists within the center.
The successful candidate will engage in a research program with a multi-disciplinary team in connecting genotypes with phenotypes, integrating multi-omics data, and applied Artificial Intelligence and machine learning-based approaches to improve switchgrass as a biofuel feedstock.
Equal Employment Opportunity Statement
All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, citizenship, age, disability or protected veteran status.
Doctorate -Plant Science related field; Bioinformatics
PhD in bioinformatics, computational biology, or plant science related fields
Experiences with omics analysis pipeline with published works; managing computational biology projects with published works; strong programming background in any language.
Experiences with artificial intelligence and machine learning; strong organization, collaboration, and interpersonal skills.
Required Application Materials
A two-page cover letter explaining how you are suitable for the position
A current resume with information on education background, publication, teaching experience, awards and/or grants, outreach experiences,
Name and contact information for at least three references.
The university is requiring all MSU students, faculty and staff to be vaccinated against COVID-19 with limited exceptions. Learn more at: https://msu.edu/together-we-will/
Please provide all application materials to be considered for the position.
Our mission is simple: creating biofuels and bioproducts that are economically viable and environmentally sustainable.
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