R&D Intern

Position Overview

The R&D (Materials Data) Intern will work at the intersection of machine learning, chemistry, and polymer science to accelerate the development of next-generation products within the adhesives market. This role will focus on developing AI and machine learning workflows that enable scientists to make data-driven formulation and materials design decisions.

The ideal candidate possesses a strong foundation in chemistry, polymer science, or materials science and has demonstrated experience applying machine learning and data science techniques to scientific problems. This position is particularly well suited for students interested in careers at the intersection of computational science, AI, and materials innovation.

Primary Responsibilities

• Collaborate with R&D scientists to collect, curate, and structure experimental data for machine learning applications.
• Develop and validate interpretable machine learning models to predict critical material and formulation properties.
• Analyze model outputs to identify meaningful structure-property relationships and generate scientific insights.
• Apply machine learning techniques to propose novel formulations and candidate materials meeting targeted performance requirements.
• Develop workflows for data preprocessing, feature engineering, model training, and model evaluation.
• Build automated data pipelines that integrate historical data sources and laboratory-generated datasets.
• Communicate technical findings and recommendations to multidisciplinary teams of scientists and engineers.

Minimum Qualifications

• Currently pursuing a Bachelor's degree in Materials Science & Engineering, Chemistry, Chemical Engineering, Polymer Science, or a related scientific discipline.
• Demonstrated proficiency in Python and common scientific computing libraries, including Pandas, NumPy, Scikit-learn, and PyTorch or TensorFlow.
• Strong understanding of machine learning fundamentals, including model development, validation, and interpretation.
• Strong theoretical understanding of chemistry, polymer science, or materials science principles.
• Excellent written and verbal communication skills and the ability to effectively collaborate with multidisciplinary scientific teams.

Preferred Qualifications

• Double major, minor, or significant coursework in Computer Science, Data Science, Statistics, or a related field.
• Research experience involving machine learning, computational chemistry, materials informatics, polymer science, or related disciplines.
• Experience working with molecular, materials, chemical, or formulation datasets.
• Familiarity with feature engineering, dimensionality reduction, optimization methods, or generative AI approaches for scientific applications.