Sort by
Refine Your Search
-
Category
-
Program
-
Field
-
AgriLife and make a difference in the world! Position Information: The successful candidate will contribute to developing and evaluating terrestrial and aquatic nitrogen dynamics and emission modeling, with
-
Advancement: Provide critical feedback, improvements, or recommendations for model advancement based on the intercomparison outcomes. Collaboration and Engagement: Actively participate in regular check-in
-
Type Staff Job Description Major/Essential Duties of Job: Conduct research to understand biodiversity responses to global change within an ecometric framework. Develop and refine ecometric models
-
for leading research with mammalian animal model systems. Use multi-omic approaches to investigate the paternal and embryonic epigenomes. Have ample opportunities to collaborate with investigators and partners
-
, test tactical and strategic interventions or augmentations. Develop models that inform decision making for improvements in agricultural systems at appropriate spatial, temporal, and organizational scales
-
motivated candidate to support the national scale assessment of carbon (C) and nitrogen (N) cycling on croplands and pasture lands using the FEST-C CONUS-EPIC model. Responsibilities: Upgrade the integrated
-
& modeling: Conduct descriptive and econometric analyses; develop reproducible code and pipelines; build dashboards/visualizations; contribute to technical reports, manuscripts, and policy briefs. Quality
-
Abilities: Proficiency level of novice in the following skills, using the novice to expert model (i.e., novice, advanced beginner, competent, proficient, expert): Initiating, building, and maintaining
-
Job Title Assistant Research Scientist- Ecosystem Modeling Agency Texas A&M Agrilife Research Department Temple Proposed Minimum Salary Commensurate Job Location Temple, Texas Job Type Staff Job
-
Type Staff Job Description Major/Essential Duties of Job: 1. Develop machine learning or physical based models for plant water stress quantification. 2. Develop machine learning models for crop mapping