use mechanistic modelling to spatialise crop yield predictions under a variety of data input scenarios
use data-driven models and machine learning to better understand the spatio-temporal impact of soil properties, weather, management, and residual herbicides on crops
publish research in peer reviewed publications
help manage the project to ensure the delivery of project milestones
Requirements
a PhD (or near completion) in precision agriculture, crop modelling, remote sensing, computer science or other quantitative disciplines
expertise in data-centric languages such as R and/or Python
expertise in analysis of spatial data such as remote sensing, and/or ‘on-farm’ data such as yield maps
preferred expertise in mechanistic modelling such as crop simulation models (e.g. APSIM, DSSAT), or other agriculture-related simulation models (e.g. soil carbon models such as Roth-C)
preferred expertise in machine learning/AI and/or statistical modelling
a track record of publications in peer-reviewed journals
experience in liaising with industry stakeholders/farmers and managing projects involving multiple people and/or organisations
Benefits
Full time, 3 years fixed term opportunity
Opportunity to join a successful and innovative team in agricultural modelling/precision agriculture