Computational Material Science Intern - Spring 2027 - Santa Clara, CA
About The Position
Nissan is seeking a Computational Materials Science Intern to join its Materials Informatics Team in Santa Clara, California, for a 12-week program in Spring 2027. The team brings together computational materials scientists, data scientists, and experimental researchers to accelerate development of next-generation materials for automotive applications, with a focus on mobility, operational excellence, customer value, and vehicle electrification. The intern will contribute to fundamental research aimed at improving critical vehicle components through materials science, machine learning, and data-driven discovery. Working alongside researchers, the intern will apply computational modeling, artificial intelligence, and high-performance computing to materials development and battery technology challenges. The role includes investigating material behavior with electronic structure calculations, developing machine-learning interatomic potentials for molecular dynamics, maintaining materials databases, analyzing datasets, and sharing research results with technical and business stakeholders. The position is intended for a student currently pursuing a PhD in Materials Science, Chemistry, Physics, Chemical Engineering, or a closely related field. Candidates must be legally authorized to work in the United States and provide proof of employment eligibility at hire. Employment is contingent on successful drug and background screenings in accordance with applicable policies and laws. Nissan states that it is an equal opportunity employer and is committed to an inclusive workplace.
Requirements
- Currently pursuing a PhD in Materials Science, Chemistry, Physics, Chemical Engineering, or a closely related field.
- Demonstrated research excellence through publications, conference presentations, patents, or other significant research contributions.
- Advanced understanding of inorganic and/or organic chemistry, reaction kinetics, and surface reactions.
- Experience performing electronic structure calculations using tools such as VASP, Gaussian, or similar computational chemistry platforms.
- Experience automating high-throughput calculations on high-performance computing (HPC) systems.
- Experience with machine learning and statistical modeling techniques.
- Advanced Python programming skills and experience with machine learning frameworks such as TensorFlow and scientific computing libraries including NumPy, SciPy, and pandas.
- Strong analytical, problem-solving, and quantitative research skills.
- Excellent written and verbal communication skills.
- Ability to work collaboratively in a multidisciplinary research environment.
- Legally authorized to work in the United States and able to provide proof of employment eligibility at hire.
Nice To Haves
- Experience with state-of-the-art generative AI models for battery material discovery and screening.
- Strong foundation in electrochemistry, organic chemistry, and surface kinetics.
- Experience with molecular dynamics simulations and materials informatics workflows.
- Familiarity with MATLAB and additional scientific computing environments.
- Passion for empirical research and solving complex scientific challenges using data-driven approaches.
Responsibilities
- Perform electronic structure calculations to investigate mechanisms behind experimentally observed material behaviors.
- Train and fine-tune machine learning interatomic potentials for large-scale molecular dynamics simulations of complex material systems.
- Build and maintain materials databases using open-source datasets and internally generated high-throughput computational results.
- Conduct descriptive analytics to identify trends, relationships, and opportunities in large materials datasets.
- Analyze, summarize, and communicate research findings through technical presentations, reports, publications, and stakeholder discussions.
- Collaborate with data scientists, materials researchers, and experimental teams to accelerate materials discovery efforts.
Benefits
- Opportunities to connect and collaborate through Business Synergy Teams (ERGs).
- Professional training and development through innovation offerings, seminars, learning platforms, and leadership development programs.
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What This Job Offers
Job Type
Full-time
Career Level
Intern
Education Level
Ph.D. or professional degree
