Postdoctoral Researcher in Computational Plant Biology & Nitrogen Use Efficiency

Postdoctoral Researcher in Computational Plant Biology & Nitrogen Use Efficiency

University of Illinois Urbana Champaign - Urbana, Illinois, USA

Position Title: Postdoctoral Researcher in Computational Plant Biology & Nitrogen Use Efficiency

Position Type: Post-Doctoral Position

Application Deadline: Tuesday, September 01, 2026

Institution: University of Illinois Urbana Champaign

Field: Plant Computational Biology

The Brooks Lab located at the University of Illinois Urbana-Champaign is seeking a postdoctoral researcher (preferred) or an exceptional graduate student to join a multi-institutional ARPA‑E‑funded project aimed at transforming nitrogen use efficiency (NUE) in maize. Our lab investigates gene regulatory networks that coordinate nitrogen use efficiency and photosynthesis, with a particular focus on how these systems will respond to climate change. This project brings together computational biology, synthetic biology and field experiments to develop novel machine learning approaches that uncover the regulatory networks linking transcription factors to nitrogen-responsive genes. The ultimate goal is to engineer maize lines capable of maintaining normal yields with 50% less nitrogen fertilizer input. The successful candidate will join an established, highly collaborative, multi‑institutional team spanning computational, molecular, and field‑based expertise. The position is funded for up to three years, with an initial one‑year appointment and the possibility of extension based on performance and fit.

Qualifications/Requirements

• Ph.D. in plant biology, computational biology, genetics, bioinformatics, or a related field (or strong candidates entering a Ph.D. program)
• Strong computational skills, including R, Python, and experience with machine learning
• Demonstrated experience with transcriptomic data analysis (RNA-seq, gene regulatory networks, etc.)
• Ability to work collaboratively across disciplines

Responsibilities

• Develop and apply computational pipelines to analyze genomic datasets
• Use machine learning approaches to identify regulatory networks controlling NUE
• Collaborate with wet‑lab and field teams to integrate computational predictions with experimental validation
• (Optional but encouraged) Participate in molecular biology, maize phenotyping, and field trials

Application Instructions

Please send the following materials as a single PDF to :

• CV with names and contact information for two references
• Cover letter describing your research experience, interests, and fit for the project