Computational Material Science Intern - Spring 2027 - Santa Clara, CA at Nissan

Job description

Location(s): Santa Clara, CA Job Schedule: 12-week program Degree Level: PhD Level Students Preferred Degree: Materials Science, Chemistry, Physics, Chemical Engineering, or related field Shape the Future of Mobility at Nissan: Launch Your Career, Drive Innovation We are currently looking for a Computational Materials Science Intern to join our Materials Informatics Team in Santa Clara, CA. With a focus on mobility, operational excellence, customer value, and vehicle electrification, the Materials Informatics team brings together computational materials scientists, data scientists, and experimental researchers to accelerate the development of next-generation materials for automotive applications. This internship offers the opportunity to contribute to fundamental research focused on improving the performance of critical vehicle components through advances in materials science, machine learning, and data-driven discovery. You will work alongside leading researchers to apply computational modeling, artificial intelligence, and high-performance computing to real-world challenges in materials development and battery technology. A Day in the Life: Perform electronic structure calculations to investigate the fundamental mechanisms behind experimentally observed material behaviors. Train and fine-tune machine learning interatomic potentials to support 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 within 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. Who We’re Looking For: Required: Must be 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 with experience using 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. Preferred: 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. What You’ll Look Forward to at Nissan : Connection and Collaboration: We offer Business Synergy Teams (ERGs) and opportunities for our employees to strengthen relationships and connect with colleagues from diverse backgrounds, fostering a culture of belonging. Professional Training and Development: Expand your knowledge and skills through Nissan’s rich innovation offerings, seminars, learning platforms, and leadership development programs. Nissan is committed to a drug-free workplace. All employment is contingent upon the