With a focus on Mobility, Operational Excellence, Value to our Customers and the Electrification of vehicles, you can expect to be part of something exciting. From the sleek design of our vehicles to the unique opportunities we offer around the globe, Nissan exemplifies ingenuity in everything we do. Our people are what drive the business forward. Our initiatives support circulatory and reuse efforts to support a more sustainable environment. Nissan 4R Energy has established a fast-paced and international division with some of the most talented people from across the globe. We believe that diversity, talent and a strong commitment to fight climate change is the key to our success.

We’re currently looking for a Battery Modeling engineer intern to join our team at Nissan Tech Center North America (NTCNA) in Farmington Hills.

This role will be responsible for analyzing battery cycling data, building equivalent circuit models, BMS models, EIS models, algorithms for battery applications in python/equivalent environment, and incorporate machine learning, feature analysis, smoothing algorithms on large datasets. The successful candidate will play a crucial role in data processing, developing models, implementing machine learning techniques, and collaborating on multiple projects.

Job functions Include but are not limited to:

• Develop Electrochemical Impedance Spectroscopy (EIS) and Equivalent Circuit Model (ECM) for battery systems using Matlab/Python/similar platforms and implement machine learning analysis for predictive modeling and processing battery cycling data.
• Analyze time-series data, apply physio-chemical models for accurate battery health predictions.
• Implement developed Battery models on real-time data and validate for robustness.
• Use data-specific smoothing algorithms and improve model accuracies.
• Calculate efficiency & accuracy of models, document every model with #comments.
• Implement the developed models on large datasets and validate for robustness.

Qualifications:

• Safety requirements: Must be able to follow safety protocols and wear safety PPE as required for lab entry.
• Job Knowledge and Skill: Strong background in physiochemical, electrochemical modeling, signal processing, matrix computations, numerical methods and machine learning algorithms, understanding of time-series data, outliers and data cleansing methods is a must. Fundamental knowledge of lithium-ion battery aging mechanisms in different environments, electrochemical and electrical characterization of batteries, comprehension of electrochemical, ECM (equivalent circuit model), thermal and Multiphysics-based models is a requirement.
• Education: Current enrolment into a Master's/PhD in Machine Learning/AI/Energy/Electrical/Chemical/Materials Engineering or any related program.
• Coding skills: Proficient in data analysis and interpretation using software tools such as Matlab, python or equivalent with strong analytical and debugging skills.
• Other Skills: Ability to work in a fast-paced environment. Stay updated on the latest advancements in battery modeling techniques and battery technologies.

Welcome to an open lane of possibility. Drive your career forward and join the company leading the technology and business evolution of the automotive industry by applying today.

Nissan is committed to a drug-free workplace. All employment is contingent upon successful completion of a drug screen for roles based in the United States and background screening for all positions.

[US ONLY] All of us at Nissan – regardless of functional area or expertise – share a passion to design, manufacture, and sell high-performance vehicles. It is Nissan’s policy to provide Equal Employment Opportunity (EEO) to all persons regardless of

race, gender, military status, disability, or any other status protected by law. Candidates for this position must be legally authorized to work in the United States and will be required to provide proof of employment eligibility at the time of hire; Nissan uses E-Verify to validate employment eligibility.

**Visa sponsorship for this position is not available at this time.*