Research Scientist I/II, Computational Organic Electronics

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.

• 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.

• 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.

• 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.

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