MLIPs Multi-Fidelity Intern

About the role

The Computational Materials Science team at Bosch Research and Technology Center North America is looking for a motivated intern to join a 1.5‑year project focused on multi‑fidelity machine‑learning interatomic potentials (MLIPs). You will work on cutting‑edge simulation workflows that support Bosch’s energy, sensor and sustainability initiatives.

Key responsibilities

• Develop and apply multi‑fidelity MLIP frameworks and automated workflows.
• Create best‑practice guidelines for MLIP setup, inference and high‑throughput execution.
• Design and implement code for high‑performance and high‑throughput computing environments.
• Review scientific literature and prepare presentations for project meetings.

Required profile

• Current PhD candidate in chemical engineering, physics, chemistry, materials science or a related discipline.
• Minimum GPA of 3.0.
• Strong foundation in materials science, solid‑state physics or chemistry.
• Excellent attention to detail, flexibility, creativity and communication skills.

Required skills

• Experience with atomistic‑scale simulations using MLIPs.
• Familiarity with electron‑based methods for training MLIPs.
• Knowledge of quantum mechanical and classical simulation techniques.
• Machine‑learning expertise applied to materials modeling.
• High‑performance and high‑throughput computing.

What we offer

• Hands‑on industry research experience alongside senior engineers.
• Opportunities to contribute to patents, software packages or publications.
• Potential pathway to long‑term career growth within Bosch.