Your Impact at LILA

Your role will involve applying computational methods and AI to accelerate the discovery and design of organic electronics materials. You will use first-principles modeling, atomistic simulations, scientific machine learning, and agentic AI systems to investigate structure-property relationships in organic and hybrid materials relevant to photovoltaics, semiconductors, optoelectronics, or electronic devices.

You will work at the intersection of physics-based simulation, AI/ML, and autonomous scientific workflows. The focus is on using computational insight to identify promising materials, explain structure-property relationships, guide optimization, and help agents reason over simulation and experimental data in scientifically grounded ways.

This is a hands-on research role for someone who can connect deep organic electronics and computational materials expertise with practical impact for customer-facing scientific programs. You will collaborate with computational scientists, AI researchers, software engineers, and experimental teams to turn simulations, models, and scientific reasoning into actionable hypotheses and discovery workflows.

What You'll Be Building

• Apply computational modeling and AI for materials discovery and design of organic semiconductors, photovoltaic materials, molecular and polymeric electronic materials, and organic electronic devices.
• Model charge transport, excited-state behavior, morphology-property relationships, and other fundamental mechanisms that influence organic electronic device performance.
• Connect simulation outputs to experimental observations and develop workflows that close the loop between computation and experiment.
• Build predictive models from computational and experimental data to guide materials selection and optimization.
• Analyze simulation and experimental data to generate actionable materials hypotheses.
• Partner with ML, software, and experimental teams on discovery workflows.
• Communicate physical insights, model limitations, and recommendations to collaborators.

What You'll Need to Succeed

• PhD or equivalent experience in Materials Science, Chemistry, Chemical Engineering, Mechanical Engineering, Physics, or a related field.
• Strong foundation in computational materials science and chemistry, including electronic structure methods and large-scale atomistic simulations.
• Deep understanding of organic semiconductors, organic electronics, photovoltaics, optoelectronic materials, charge transport, or related device-relevant materials systems.
• Experience applying first-principles, molecular simulations, or general atomistic methods to materials discovery, optimization, or understanding.
• Ability to connect molecular, morphological, and electronic structure features to device-relevant properties.
• Strong programming skills in Python and scientific computing workflows.

Bonus Points For

• Experience studying organic photovoltaics, organic semiconductors, polymer electronics, molecular electronics, perovskite-organic interfaces, or related materials systems.
• Experience applying AI/ML to computational materials science, molecular simulations, or other physics-based simulations.
• Strong familiarity with agentic AI systems, autonomous scientific workflows, or simulation-aware agents.
• Experience integrating computational predictions with experimental characterization, device measurements, or closed-loop optimization workflows.
• Familiarity with charge transport modeling, excited-state calculations, morphology generation, coarse-graining, and/or multiscale and multiphysics simulations.
• Ability to communicate physical insight, uncertainty, and model limitations to cross-functional collaborators.