Machine Learning Cfd Jobs in Massachusetts (NOW HIRING)

Your Impact at LILA

Lila Sciences is seeking a Computational Scientist I/II, Soft Matter Formulations - Complex Fluids to develop models, tools, and workflows that accelerate discovery across liquid and flowable soft material systems. This role focuses on complex fluids, including colloidal suspensions, emulsions, surfactant systems, polymer solutions, coolants and heat-transfer fluids, coatings, inks, and lubricants.

You will bring domain expertise in soft matter, complex fluids, colloids, rheology, interfacial science, formulation science, or a closely related area, and apply machine learning methods to connect composition, microstructure, processing conditions, and bulk fluid properties. The work spans rheology and flow behavior, phase stability, dispersion and aggregation, sedimentation, shelf-life, interfacial and wetting behavior, surface tension, foaming, and thermophysical performance.

This is a hands-on scientific ML role for someone who can bridge domain context and computational execution. You will develop structure-property models linking composition to microstructure and bulk fluid behavior, build active learning workflows over continuous compositional spaces, and incorporate mesoscale or continuum simulation coupling, such as coarse-grained molecular dynamics, dissipative particle dynamics, or CFD hooks, where it improves prediction and experimental decision-making.

What You'll Be Building

• 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.
• Define modeling targets for rheology, phase stability, dispersion and aggregation behavior, sedimentation, shelf-life, and thermophysical performance for liquid formulation systems,
• Build structure-property models that connect composition, microstructure, processing conditions, and bulk fluid properties.
• Design active learning workflows over continuous compositional spaces that prioritize high-value experiments and formulation decisions.
• Incorporate mesoscale and continuum simulation outputs, such as coarse-grained MD, dissipative particle dynamics, or CFD-linked features, where they improve prediction or interpretation.
• Create tools that help scientists interpret complex fluid data and prioritize formulation, processing, or composition decisions.
• Partner with experimental teams to align models with measurement workflows, formulation workcell throughput, material performance requirements, and practical development needs.
• Communicate model behavior, uncertainty, and recommendations to scientific, engineering, and cross-functional collaborators.

What You'll Need to Succeed

• 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.

Bonus Points For

• 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.