Multiphysics Modeling Scientist

Solid Power•Louisville, KY

20h•Hybrid

About The Position

Solid Power is seeking a Multiphysics Modeling Scientist to join their talented team working to revolutionize the battery industry through the development of next-generation, all solid-state rechargeable batteries. Solid Power is a dynamic, fast-paced, collaborative, and innovative team environment. The preferred candidate will have significant experience working with and using multiphysics simulations to model chemical systems, heat transfer dynamics, and electrochemical devices. The candidate will be expected to guide synthetic approaches for dry, inorganic powders by modeling conditions in high-throughput powder reactors. They will also work to update existing thermal abuse models that integrate atomic-scale reaction kinetics with continuum-scale heat transfer dynamics. The candidate should be comfortable building P2D/P4D DFN models and using these approaches to guide experimental planning, especially as it relates to battery formulation improvements.

Requirements

Master’s or Ph.D. in Computational Chemistry, Materials Engineering, Chemical Engineering or a related Electrochemical discipline.
2+ years of professional experience using commercial software to develop multiphysics models of lithium-ion batteries.
A deep understanding of electrochemistry, preferably with at least some experience in battery systems.
Proficiency with Python and common data science libraries such as Pandas, NumPy, and SciPy.
Strong analytical and statistical problem-solving skills, having specific experience with configuring solvers for regression and parametrization tasks.
Experience working with COMSOL is necessary.
The ability to formulate coupled multiphysics models from first principles, including governing equations, assumptions, constitutive relationships, source terms, and boundary/initial conditions, prior to software implementation is a must.
Rapid learner and a self-motivated individual who takes ownership in a collaborative start-up environment.
Excellent communication, negotiation, and organizational skills with strong attention to detail.

Nice To Haves

Experience working in Ansys, work with PyBaMM, and general python knowledge are all a strong plus.
Previous experience building 1D PDE/ODE models from first principles and manually implementing them is a strong plus (e.g., Numpy implementation for Gummel iteration, Newton iteration, segregated solver).
Familiarity with agentic code development concepts and the limitations of LLMs is a plus.
The candidate should be someone who stays up to date on the latest continuum-scale modeling approaches.

Responsibilities

Develop continuum-scale reactor models for dry inorganic powder synthesis, coupling heat transfer, gas flow, mass transport, and reaction kinetics to guide reactor conditions and experimental planning.
Upgrade existing COMSOL-based implementations for thermal abuse modeling of ASSB cells.
Develop and validate mathematical models (both fundamental physics-based and data driven) to predict thermal, mechanical, and electrochemical behavior of ASSB materials, sub-assemblies, and finished cells.
Work with subject matter experts to develop experimental plans to isolate and quantify key model parameters, while producing minimal solvers that can be used by scientists/engineers.
Collaborate with subject matter experts to refine and continually validate modeling approaches, while employing best-practice workflows (i.e., mesh sensitivity analysis, ablation testing, unit testing, parameter sensitivity quantification).
Clearly explain to internal stakeholders the uncertainty in modeling predictions and write documentation on proposed upgrades that could be used to improve certainty.
Collaborate with data scientists on strategies to accelerate multiphysics modeling solvers and on strategies to guide virtual experiment prioritization (e.g. Bayesian approaches).
Collaborate with atomic-scale and coarse-grained modeling team members to translate their outputs into continuum-scale model inputs, assumptions, parameters, and validation targets.