Intern - Polymer Characterization

E Ink•Fremont, CA

15h•Onsite

About The Position

We are seeking a highly motivated intern to support materials development through a rigorous blend of hands-on characterization and disciplined data workflow design. This role focuses on polymer-based systems within a wet chemistry environment, with particular emphasis on establishing standardized impedance spectroscopy (EIS) measurement protocols, creating structured experimental datasets, and building the data-processing foundation needed for future machine-learning-guided material optimization. The position is primarily experimental (~70%) with meaningful exposure to data systems and automation (~30%).

Requirements

Currently pursuing a Master's/PhD degree in Materials Science and Engineering, Chemical Engineering, Chemistry, Physics, Electrical Engineering, Polymer Science, or a related field.
Hands-on laboratory, internship, co-op, or undergraduate/graduate research experience in materials characterization, polymer/material systems, or related experimental work.
Foundational experience with impedance spectroscopy (EIS) or related electrochemical/electrical characterization methods.

Nice To Haves

Hands-on experience with atomic force microscopy (AFM), particularly quantitative nanomechanical mapping (QNM) mode
Strong execution mindset with attention to data quality, repeatability, documentation, and the ability to work effectively with real-world, imperfect datasets.
Proficiency in Python (pandas, numpy, matplotlib) applied to experimental data processing, automation, and structured data handling.
Experience building machine learning workflows, feature engineering pipelines, or data-processing tools for scientific datasets.
Familiarity with structure-processing-property relationships and materials optimization concepts.
Experience working with larger experimental datasets across multiple testing conditions, instruments, or source formats.

Responsibilities

Prepare, handle, and evaluate samples in a wet chemistry lab environment.
Establish, document, and standardize impedance spectroscopy (EIS) test methods and measurement conditions for material characterization.
Conduct repeated EIS measurements with high consistency, reproducibility, and attention to experimental controls.
Process and analyze experimental data, including impedance and related characterization datasets.
Extract key material properties and help build a basic feature-extraction framework that converts raw signals into structured, comparable descriptors.
Build structured datasets with consistent naming conventions, traceable metadata, and organized records that support downstream analysis.
Develop robust Python workflows (pandas, numpy, matplotlib, etc.) for automated data cleaning, transformation, structuring, and analysis.
Build data-quality and validation checks to identify inconsistencies, missing data, outliers, and measurement issues, and flag them clearly for the broader team.
Collaborate closely with researchers to improve experimental protocols, strengthen data reliability, and support future machine learning workflows for material optimization.