Your Impact at LILA

Your role will involve applying soft matter physics and molecular simulation expertise to accelerate the discovery, optimization, and understanding of complex soft matter systems. You will use coarse-grained and atomistic modeling, mesoscale simulation, analytical theory, and agentic AI systems to investigate polymers, colloids, surfactants, gels, liquid crystals, and formulated fluid systems with direct relevance to commercial applications.

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 formulations and material compositions, explain structure-property relationships, guide optimization campaigns, and help agents reason over simulation and experimental data in scientifically grounded ways. Desired simulation expertise covers atomistic and coarse-grained molecular dynamics, mesoscale approaches such as dissipative particle dynamics, Monte Carlo sampling, and analytical theory including self-consistent field theory, field theoretic simulation, and liquid state theory.

This is a hands-on research role for someone who can connect deep soft matter and molecular simulation expertise with software development and practical scientific impact. You will collaborate with computational scientists, AI researchers, software engineers, and experimental teams to turn simulations, models, and scientific reasoning into robust software, agentic frameworks, and closed-loop scientific workflows.

What You'll Be Building

• Implement, extend, and deploy atomistic, coarse-grained, and mesoscale simulation capabilities for soft matter systems, including MD, DPD, SCFT, Monte Carlo, and liquid state theory
• Engineer simulation software with modern scientific libraries, emphasizing testing, modularity, and reproducibility at scale on HPC and cloud infrastructure
• Translate simulation outputs into descriptors and datasets for ML models, and build predictive models to guide formulation selection and optimization
• Develop and deploy agentic AI systems that leverage simulation capabilities for autonomous scientific discovery and closed-loop experimentation
• Communicate physical insights, model limitations, and recommendations to computational, ML, and experimental collaborators

What You'll Need to Succeed

• PhD in chemical engineering, physics, materials science, chemistry, or a related field (or master's with equivalent experience)
• Strong background in soft matter physics covering polymers, colloids, surfactants, gels, or liquid crystals, with hands-on expertise in atomistic and coarse-grained MD
• Working knowledge of at least two of: DPD or other mesoscale methods, Monte Carlo, SCFT, field theoretic simulation, or liquid state theory
• Strong Python scientific software development skills; fluency with modern practices including version control, testing, code review, and packaging
• Experience running and automating simulations at scale on HPC or cloud, and integrating computational predictions into experimental or closed-loop optimization workflows
• Good familiarity with agentic AI systems, autonomous scientific workflows, or simulation-aware agents

Bonus Points For

• Experience with a compiled language (C++, Rust, or Fortran) for performance-critical simulation code, or GPU acceleration via JAX or PyTorch
• Contributions to open source simulation packages such as LAMMPS, HOOMD-blue, GROMACS, or OpenMM
• Experience developing ML potentials or applying AI/ML to soft matter simulation data and molecular property prediction
• Experience building agents that reason over or orchestrate simulation workflows, or coupling simulation to active learning and Bayesian optimization pipelines
• Publication record in soft matter theory and simulation