Advisor – Computational Scientist, Multiscale & Multiphysics Modeling for Drug Delivery Full-time

Computational Scientist

At Lilly, the work is demanding because patients are waiting. We unite caring with discovery to help make life better for people around the world, knowing that every decision, every detail, and every day matters. Headquartered in Indianapolis, Indiana, our over 50,000 employees around the globe take on complex challenges to discover and deliver life-changing medicines, strengthen how health is understood and managed, and support the communities we serve. This is hard, urgent, selfless work—but it's work worth doing. If you're driven by purpose and ready to bring your best to work that truly matters for patients, we invite you to join us.

The Delivery, Device, and Connected Solutions (DDCS) organization supports the design, development, and commercialization of drug products and pharmaceutical delivery systems, including medical devices, combination products, and container closure systems.

This position sits within the Computational Modeling and Simulation team, which builds mechanistic understanding of the physicochemical processes underlying drug delivery. This role develops multiscale and multiphysics models—connecting molecular properties, formulation composition, and device and drug-delivery conditions to the safety and efficacy profiles through simulations of delivery, transport, absorption, and pharmacokinetics. These models generate the computational evidence that informs device-bridging strategy, delivery system design, and regulatory discussions.

Multiscale and multiphysics modeling within DDCS supports drug delivery systems across multiple routes of administration. Primary focus areas include subcutaneous (SubQ) injection systems, where modeling connects formulation and device/injection parameters to tissue-level transport and systemic absorption. Additional routes include transdermal, nasal, and pulmonary, as well as local and central nervous system (CNS) delivery. The computational scientist will develop methodologies that scale across these modalities and length scales.

Key Responsibilities:

• Develop, execute, and interpret multiscale, multiphysics models that link molecular properties, formulation composition, and drug delivery/injection parameters to transport, absorption, and pharmacokinetic outcomes.
• Apply and integrate computational approaches—such as CFD, FEA, particle transport, heat transfer, and species diffusion and reaction—as needed to characterize the continuum domain processes underlying drug delivery and absorption.
• Build coupled modeling frameworks (e.g., mechanistic PBPK coupled with device- or tissue-level physics) that translate device and formulation inputs directly into patient-relevant pharmacokinetic predictions.
• Develop platform tools built for a class of problems—reusable across molecules, doses, formulations, and delivery routes—rather than one-off, case-specific models.
• Build automated modeling workflows, scripted pipelines, and differentiable calibration frameworks (e.g., gradient-based estimation, sensitivity and identifiability analysis) to improve efficiency and reproducibility.
• Apply and develop AI/ML techniques, including surrogate modeling, reduced-order models, and physics-informed approaches, to accelerate simulation and design iteration.
• Identify which physical or analytical measurements would resolve open modeling questions, and partner with analytical and clinical teams to define and integrate them.
• Translate model outputs into decision-ready arguments—including device-bridging strategies, bioequivalence assessments, and delivery-system design recommendations—to support regulatory discussions.
• Communicate modeling assumptions, methods, and results clearly to cross-functional and non-technical stakeholders.
• Author and review technical reports and regulatory submission content related to computational modeling.
• Contribute to model governance and credibility assessment frameworks aligned with ASME V&V 40.
• Collaborate with Formulation Science, Device Engineering, Quality, Regulatory, and Manufacturing.
• Maintain a quality mindset, proactively identifying issues and supporting continuous improvement.

Basic Qualifications:

• Earned Master's degree in Mechanical Engineering, Chemical Engineering, Aerospace Engineering, Biomedical Engineering, or a related field with 10+ years of relevant multiscale or multiphysics modeling experience.
• Experience with mechanistic PBPK or compartmental modeling connecting formulation and device properties to absorption and pharmacokinetics. Strong foundation in transport phenomena, fluid mechanics, and heat and mass transfer in complex or biological media.
• Scripting and customization of multiphysics solvers—for example, Abaqus Python scripting and user subroutines (Fortran), COMSOL LiveLink (MATLAB/Java), or Fluent UDFs (C).
• Strong proficiency in Python for model automation, parametric studies, post-processing, and analysis (NumPy, SciPy, pandas, matplotlib), with disciplined use of version control and reproducible workflows.
• Effective use of HPC environments (job scheduling with Slurm or equivalent, distributed solver execution
• Experience building and calibrating models against experimental or clinical data, including sensitivity and identifiability analysis.
• Experience with sensitivity analysis, uncertainty quantification, and surrogate or reduced-order modeling; differentiable programming frameworks such as JAX for inverse problems and physics-informed models.

Additional Preferences:

• Earned PhD in a related field with 3+ years of research experience in relevant computational modeling
• Working knowledge of ASME V&V 40 and experience developing credibility arguments for computational models.
• Familiarity with regulatory guidance on computational modeling and simulation for medical devices and drug products.
• Experience connecting molecular- or formulation-scale properties to device- and patient-scale outcomes through coupled, multiscale modeling.
• Experience tissue mechanics and fluid-structure interaction in biological systems.
• Demonstrated application of AI/ML techniques to simulation (surrogate models, reduced-order models, physics-informed neural networks).
• Track record of translating model outputs into device bridging strategy, delivery system design, or regulatory arguments (e.g., biowaiver assessments).
• Peer-reviewed publication record in computational or biomedical modeling.
• Knowledge of drug delivery device design and pharmaceutical formulation considerations, including peptide and biologic delivery.