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Lila Sciences is hiring a Computational Scientist to develop machine learning models and workflows that accelerate discovery in soft matter and complex fluid systems. The role bridges domain expertise in colloids, emulsions, surfactants, polymer solutions, rheology, and interfacial science with modern ML and computational methods.
You will develop structure-property models that connect composition, microstructure, processing conditions, and bulk fluid properties for systems including colloidal suspensions, emulsions, surfactant systems, polymer solutions, coolants, heat-transfer fluids, coatings, inks, and lubricants. Key responsibilities include building active learning workflows over continuous compositional spaces, incorporating mesoscale and continuum simulations (coarse-grained MD, dissipative particle dynamics, CFD), and creating tools to help scientists interpret complex fluid data and prioritize formulation decisions.
You will partner with experimental teams to align models with measurement workflows, formulation throughput, and material performance requirements. Work spans rheology and flow behavior, phase stability, dispersion and aggregation, sedimentation, shelf-life, interfacial and wetting behavior, surface tension, foaming, and thermophysical performance.
Required: PhD in chemical engineering, materials science, physics, applied mathematics, or related field (or master's with equivalent experience); strong Python and modern ML framework experience; domain expertise in soft matter, complex fluids, colloids, rheology, or formulation science; ability to train and evaluate models on experimental or simulation datasets; strong communication skills across scientific and cross-functional teams.
Bonus: hands-on experimental experience in complex fluids; experience with active learning over compositional spaces; familiarity with high-throughput formulation campaigns; experience modeling thermophysical properties for coolant or heat-transfer applications.
