Job Description

Bosch Research is looking for an intern in atomistic computational materials science to join the materials design team. Our goal is to enable improved Bosch products through deep understanding of thermodynamic, kinetic, and transport phenomena on an atomic level using both quantum and classical simulations. Strong focus is placed on development and application of computational and machine-learning methods for understanding and automated discovery of next-generation materials, primarily for electrochemistry and energy conversion.

IN THIS POSITION, THE INTERN WILL FOCUS ON UTILIZING MACHINE LEARNING ALGORITHMS AND LARGE DATA RESOURCES OF ORGANIC MATERIALS TO EXPLORE PROPERTY PREDICTIONS AND PROCESS OPTIMIZATION IN THE LARGER CONTEXT OF CHEMOINFORMATICS. INNOVATIVE APPROACHES WILL BE EXPLORED TO EXTRACT MOLECULAR FINGERPRINTS AND CREATE THE MAPPING BETWEEN THE MATERIAL AND LATENT SPACES. THIS WORK IS RELEVANT FOR THE DEVELOPMENT OF THE DIVERSE ORGANIC MATERIALS PORTFOLIO OF BOSCH.

As part of Bosch Corporate Research, we are dedicated to long-term fundamental investigations of transformative energy technologies. Located in Cambridge, close to MIT and Harvard, our materials computation team supports global experimental efforts with fundamental understanding, emphasizing innovation and high technological impact. Using both internal funding and government grants, we collaborate closely with a network of leading computational and experimental teams which includes top universities, national labs and industrial partners. We strongly encourage high-impact publications and patent applications.

Qualifications

REQUIRED QUALIFICATION:

• Ph.D. candidate at a top university in chemical engineering, physics, chemistry, materials science, or a related field.
• Solid foundations in materials science, solid-state physics, and/or chemistry
• EXPERIENCE WITH METHOD DEVELOPMENT AND FAMILIARITY WITH CHEMOINFORMATIC AND ML TOOLS .
• Attention to detail, flexibility, creativity, and excellent communication and teamwork skills

DESIRED QUALIFICATION:

• Experience in atomistic simulations, preferably density-functional theory, molecular dynamics, and/or quantum chemistry
• Significant research and/or coding experience, preferably with fluency in Python and FORTRAN/C++
• Strong background in physics and coding, and passion on working and understanding physics-based devices

Additional Information

By choice, we are committed to a diverse workforce - EOE/Protected Veteran/Disabled.

BOSCH is a proud supporter of STEM (Science, Technology, Engineering & Mathematics) Initiatives

• FIRST Robotics (For Inspiration and Recognition of Science and Technology)
• AWIM (A World in Motion)