Develop models that connect compound properties, formulation, release rates, and measured insect responses to field performance.
Simulate the conditions that matter for each application, including wind, temperature, humidity, sunlight, rainfall, and vegetation, and their effects on chemical transport and persistence.
Connect predicted exposure to insect movement, approach, landing, feeding, or egg laying using species-specific experimental evidence.
Work with chemists, entomologists, and experimental scientists to calibrate models, identify missing measurements, and design experiments that resolve the largest uncertainties.
Evaluate predictions against held-out experiments and prospective field trials. Quantify uncertainty and establish when a model can support a decision and when more evidence is needed.
Build reproducible simulation and analysis software that helps the team compare compounds, formulations, application methods, and field-trial designs.
Qualifications
Master’s, Ph.D., or equivalent research and engineering experience in applied mathematics, physics, mechanical or chemical engineering, computational science, or a closely related field.
Demonstrated experience building numerical simulations of physical or biological systems and checking their predictions against experimental observations.
Strong scientific programming skills in Python, C++, or comparable tools, with the ability to build reliable, reusable software.
A strong foundation in relevant modeling methods, such as fluid dynamics, transport processes, differential equations, stochastic modeling, or agent-based simulation.
Experience estimating model parameters, working with noisy experimental data, and assessing uncertainty and predictive performance.
Ability to work across disciplines and develop the chemical and biological understanding needed to make the simulations useful.
Desired Attributes
Experience with chemical release and transport, atmospheric or environmental modeling, agricultural systems, insect behavior, or chemical ecology.
A record of translating simulations into predictions that hold up in physical experiments, including in settings where conditions change.
Experience combining mechanistic models with machine learning or surrogate models to make simulation and experiment selection more effective.
Good judgment about which processes need detailed modeling and where a simpler model is sufficient.
Intellectual rigor and comfort changing an approach when the evidence does not support it.
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