Computational Material Science Intern
Posted on Apr 5, 2021 by Robert Bosch
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 particular position, the intern will be responsible for developing new atomistic methods to calculate the thermal transport in alloyed semiconductor materials using tools such as density functional theory (DFT), which will be important for the development of power electronic semiconductor devices.
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.
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
Attention to detail, flexibility, creativity, and excellent communication and teamwork skills
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
Duration: Typically, 14 weeks