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

Lila Sciences
Cambridge
Workplace: OnsiteFull timeUSD 118,800 - 187,000Function: Research & Scientific (R&D)Education: phdSkills: ["Communication","Cross-functional collaboration","Scientific reasoning"]

Develop scientific ML models and workflows for complex fluids and soft matter formulations. Build structure-property models that link composition, microstructure, and processing conditions to properties like rheology, phase stability, dispersion/aggregation, sedimentation, shelf-life, wetting, surface tension, foaming, and thermophysical performance. Create active learning pipelines across continuous compositional spaces and integrate mesoscale/continuum simulation outputs (e.g., coarse-grained MD, dissipative particle dynamics, CFD-linked features) to improve experimental decision-making and collaborate with experimental teams.

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Lila Sciences
Lila Sciences
1 month ago

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

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Last checked: 14 hours agoStatus: Live

Job Summary

Develop scientific ML models and workflows for complex fluids and soft matter formulations. Build structure-property models that link composition, microstructure, and processing conditions to properties like rheology, phase stability, dispersion/aggregation, sedimentation, shelf-life, wetting, surface tension, foaming, and thermophysical performance. Create active learning pipelines across continuous compositional spaces and integrate mesoscale/continuum simulation outputs (e.g., coarse-grained MD, dissipative particle dynamics, CFD-linked features) to improve experimental decision-making and collaborate with experimental teams.
Location: Cambridge
Workplace: Onsite
Employment Type: Full time
Job Function: Research & Scientific (R&D)
Seniority: Mid level

Key Responsibilities

  • •Develop machine learning models for complex fluid systems across colloidal suspensions, emulsions, surfactant systems, polymer solutions, coolants/heat-transfer fluids, coatings, inks, and lubricants.
  • •Define targets for predicting rheology, phase stability, dispersion/aggregation, sedimentation, shelf-life, and thermophysical performance for liquid formulation systems.
  • •Build structure-property models connecting composition, microstructure, processing conditions, and bulk fluid properties.
  • •Design active learning workflows over continuous compositional spaces to prioritize high-value experiments and formulation decisions.
  • •Partner with experimental teams and communicate model behavior, uncertainty, and recommendations; incorporate mesoscale/continuum simulation outputs where they improve prediction or interpretation.

Pay and Benefits

Salary: USD 118,800 - 187,000
Perks:Health InsuranceDentalVisionLife InsuranceDisability InsurancePaid ParentalCommuter BenefitsMeal Allowance

Key Requirements

  • •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 fluid properties including rheology, phase stability, dispersion/aggregation, sedimentation, wetting, surface tension, foaming, thermal conductivity, and heat capacity.
  • •Strong Python skills and experience with modern machine learning frameworks.
  • •PhD in chemical engineering, materials science, physics, applied mathematics, computational science, or a related field, or a master’s degree with equivalent relevant experience.
Experience:Scientific MLMaterialsSoft matterComplex fluidsFormulation
Education:PhD / Doctorate
Skills:CommunicationCross-functional collaborationScientific reasoning
Languages:English
Tech Stack:PythonMachine learningCoarse-grained molecular dynamicsDissipative particle dynamicsCFD

Company Brief

Lila Sciences
Develops AI-driven platforms to accelerate drug discovery and biological research by integrating machine learning with chemical and biological data to predict molecular properties, streamline candidate selection, and enable faster therapeutic development.
Industry: Biotech
Company Size: Small (11 to 50 employees)
Growth: Early Stage Startup
Funding: Seed
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