Research Engineer, Field Simulation

The role

Build computer simulations to predict how compounds affect insects under real field conditions. Work with scientists to connect compound release and movement to insect behavior and test predictions against experimental data.

What you will do

• Develop models connecting compound properties, formulation, release rates, and insect responses to field performance.
• Simulate conditions including wind, temperature, humidity, sunlight, rainfall, and vegetation.
• Connect predicted exposure to insect movement, approach, landing, feeding, or egg laying.
• Work with chemists, entomologists, and experimental scientists to calibrate models and design experiments.
• Evaluate predictions against held-out experiments and prospective field trials.
• Quantify uncertainty and establish when a model can support a decision.
• Build reproducible simulation and analysis software.

Required 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 predictions against experimental observations.
• Strong scientific programming skills in Python, C++, or comparable tools.
• Strong foundation in 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 chemical and biological understanding.

Nice to have

• 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.
• Experience combining mechanistic models with machine learning or surrogate models.
• 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.

About the company

Monarch (monarchlabs.org) offers opportunities to do life's work while helping solve technical and moral challenges.