Computational Scientist I/ii, Soft Matter Formulations , Complex Fluids

Lila Sciences Lila Sciences · AI Frontier · Alewife, Cambridge, MA · Physical Sciences AI

Seeking a Computational Scientist to develop models, tools, and workflows using machine learning for soft material systems, focusing on complex fluids. The role involves connecting composition, microstructure, processing, and fluid properties, building active learning workflows, and potentially incorporating simulation outputs to accelerate discovery and guide experimental decisions.

What you'd actually do

  1. Develop machine learning models for complex fluid systems, including colloidal suspensions, emulsions, surfactant systems, polymer solutions, coolants and heat-transfer fluids, coatings, inks, and lubricants.
  2. Define modeling targets for rheology, phase stability, dispersion and aggregation behavior, sedimentation, shelf-life, and thermophysical performance for liquid formulation systems,
  3. Build structure-property models that connect composition, microstructure, processing conditions, and bulk fluid properties.
  4. Design active learning workflows over continuous compositional spaces that prioritize high-value experiments and formulation decisions.
  5. Incorporate mesoscale and continuum simulation outputs, such as coarse-grained MD, dissipative particle dynamics, or CFD-linked features, where they improve prediction or interpretation.

Skills

Required

  • Experience applying machine learning to scientific, materials-focused, complex fluid, soft matter, or formulation problems.
  • Domain expertise in colloids, emulsions, surfactants, polymer solutions, rheology, interfacial science, thermophysical fluids, coatings, inks, lubricants, or related fields.
  • Familiarity with rheology, phase stability, dispersion, aggregation, sedimentation, wetting, surface tension, foaming, thermal conductivity, heat capacity, or related fluid performance properties.
  • Strong Python skills and experience with modern ML frameworks.
  • Experience training, evaluating, and improving models using experimental, simulation, or scientific datasets.
  • Ability to use simulations, theory, descriptors, or mechanistic understanding to inform modeling choices for complex fluid systems.
  • Strong communication skills with experimental, computational, and cross-functional collaborators.
  • PhD in chemical engineering, materials science, physics, applied mathematics, computational science, or a related field, or a master’s degree with equivalent relevant experience.

Nice to have

  • Experience working with experimental data from colloidal suspensions, emulsions, surfactant systems, polymer solutions, coolants, coatings, inks, lubricants, or related liquid formulations.
  • Experience modeling composition-to-microstructure-to-property relationships for liquid or flowable soft material systems.
  • Familiarity with active learning over continuous compositional spaces or high-throughput formulation campaigns.
  • Experience incorporating mesoscale or continuum simulation outputs, including coarse-grained MD, dissipative particle dynamics, CFD-linked models, or related approaches, into ML workflows.
  • Experience modeling thermophysical fluid properties relevant to coolant or heat-transfer applications.
  • Hands-on experimental experience in complex fluids, colloids, emulsions, rheology, interfacial science, or soft material formulation domains.

What the JD emphasized

  • apply machine learning methods to connect composition, microstructure, processing conditions, and bulk fluid properties
  • build active learning workflows over continuous compositional spaces
  • incorporate mesoscale or continuum simulation coupling

Other signals

  • apply machine learning methods to connect composition, microstructure, processing conditions, and bulk fluid properties
  • build active learning workflows over continuous compositional spaces
  • incorporate mesoscale or continuum simulation coupling