Dow Career Experiences for PhDs Modeling Approaches for Multilayer Film Performance

About Dow

At Dow, we believe in putting people first and we’re passionate about delivering integrity, respect and safety to our customers, our employees and the planet. Our people are at the heart of our solutions. They reflect the communities we live in and the world where we do business. Their diversity is our strength. We’re a community of relentless problem solvers that offers the daily opportunity to contribute with your perspective, transform industries and shape the future. Our purpose is simple – to deliver a sustainable future for the world through science and collaboration. If you’re looking for a challenge and meaningful role, you’re in the right place.

About the Role

Dow is excited to pilot our Dow Career Experiences program with UIUC, adapting our proven internal gig model to create industry‑sponsored opportunities for U.S. graduate students to work on industrially relevant problems, right from their university campus.  As industrial employers increasingly seek work-ready graduates, our University Gig Program helps provide students more insight to the evolving skill demands of the workforce and the real-world challenges.

A significant portion of Dow’s polyethylene product volume is used in film applications, particularly multilayer films. In these structures, layers of carefully selected properties and defined thicknesses are combined to achieve product properties beyond individual material potentials.

Multilayer films are complex systems and selection of components and structures are often connected to deep subject matter expertise. In order to preserve knowledge and enable systematic evaluations numerous fundamental models have been developed over the years, many within Dow’s research organization. With the rise of machine learning, data driven methods became an additional tool in the toolbox for modeling these types of systems.

The goal of this work is to conduct a thorough evaluation of data-driven and hybrid fundamental / machine learning methods for modeling the performance of polymeric multilayer films with a special focus on packaging applications and polyolefins. Dow will provide a dataset for benchmarking of the methods, and potentially contribute fundamental or semi-empirical relationships to complement machine learning models.

Requirements

• PhD students currently enrolled in chemistry, chemical engineering, and material science engineering, graduating after 2026
• Part‑time commitment (5-10 hours/week, 7-10 weeks)
• Short‑term, project‑based work, paired with Dow mentor(s)
• Paid opportunities (32/hr)
• Projects completed on the student’s university campus

Responsibilities

• Hands‑on, experiential learning focused on industrially relevant skillsets
• Opportunity to make real‑world impact
• Deeper understanding of a career in industry
• Exposure to and expansion of a professional industry network
• Literature review of machine learning in polymeric multilayer performance modeling.
• Computational implementation of the methods in a designated environment.
• Integration of fundamental and semi-empirical methods.
• Evaluation of prediction accuracy, data requirements, and interpretability of the various workflows.

How to Apply:

Please send your resume/CV and a cover letter (optional) to Qiang Liu (qliu3@dow.com), Associate R&D Director of External Technology.