Research Associate-Fixed Term

Michigan State University(MSU) • East Lansing, MI • 16h ago

Position Summary

The Plant Resilience Institute (PRI) at Michigan State University is seeking a highly motivated and interdisciplinary postdoctoral researcher with strong computational and data science skills to join a project focused on identifying causal environmental drivers of crop resilience and yield. The project integrates field-based environmental monitoring, quantitative trait data, and controlled environmental simulations to uncover key environmental variables influencing plant performance across diverse agricultural conditions.

This position will focus on the computational aspects of the project, including data integration, association analyses (EWAS), and development of predictive models using large, multi-dimensional datasets. The postdoc will work closely with PRI researchers and external partners to translate complex environmental and phenotypic data into actionable insights for crop improvement and climate adaptation strategies.

This is an exciting opportunity to contribute to a pioneering approach in plant science that connects quantitative genetics, environmental sensing, and agricultural data science at scale.

Key Responsibilities:

Integrate and analyze large-scale environmental and biological datasets collected from multi-site field trials and controlled-environment experiments

Perform association analyses (e.g., EWAS) to identify environmental variables significantly correlated with plant traits such as yield and stress resilience

Collaborate with sensor teams, plant scientists, and data engineers to develop robust, reproducible data workflows

Contribute to the development of computational tools and pipelines for spatial, temporal, and multi-omic data

Support the synthesis of pilot findings and contribute to future research directions and proposals

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.

Required Degree

Doctorate -Molecular Biology, Genetics, Cell Biology or related field

Minimum Requirements

Ph.D. in plant biology, quantitative genetics, computational biology, statistics, bioinformatics, data science, agricultural engineering, environmental modeling, or a related field with a strong computational and/or quantitative component by the time of appointment

Strong data analysis skills using R, Python, or equivalent tools for large-scale datasets

Familiarity with large, multidimensional datasets (e.g., environmental, phenotypic, or genetic)

Strong communication, organizational, and team collaboration skills

Desired Qualifications

Experience working with large scale multi-environment trials

Familiarity with environmental sensors, remote sensing, or IoT-based data collection

Knowledge of association mapping (GWAS/EWAS), statistical modeling, or machine learning in biological @contexts

Interest in field-lab integration and scaling projects from pilot to multi-site implementation

Experience working with maize or other major crop species

Required Application Materials

Cover letter

Special Instructions

Required application materials: Please submit a cover letter detailing research experience and motivation for joining this project, a curriculum vitae and contact information for three references. The position is open immediately and will close upon the determination of an ideal candidate. Applications will be reviewed upon

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