Bosch Computational Material Science Intern in Cambridge, Massachusetts
The Bosch Research and Technology Center North America with offices in Sunnyvale, California, Pittsburgh, Pennsylvania and Cambridge, Massachusetts is part of the global Bosch Group ( www.bosch.com ), a company with over 70 billion euro revenue, 400,000 people worldwide, a very diverse product portfolio, and a history of over 125 years. The Research and Technology Center North America (RTC-NA) is committed to providing technologies and system solutions for various Bosch business fields primarily in the areas of Human Machine Interaction (HMI), Robotics, Energy Technologies, Internet Technologies, Circuit Design, Semiconductors and Wireless, and MEMS Advanced Design.
Bosch Research seeks for one or more interns specializing in atomistic computational materials science to join the materials design team. Our goal is to improve 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, including for electrochemistry and energy conversion.
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 laboratories, and industrial partners. We strongly encourage high-impact publications and patent applications.
Development and application of atomistic methods for understanding thermodynamics, kinetics, and transport
Computational discovery of next-generation materials system for various applications
Designing and implementing code for high-performance and high-throughput computing
Application of machine-learning methods to computational materials science
Writing reports, reviewing literature, and preparing presentations of results for project team meetings
Ph.D. candidate at a top university in chemical engineering, physics, chemistry, materials science, or a related field.
Experience in atomistic simulations, including at least one of: density-functional theory, molecular dynamics, or quantum chemistry
Solid foundations in materials science, solid-state physics, and/or chemistry
Attention to detail, flexibility, creativity, and excellent communication and teamwork skills
Significant research experience, including high-impact publications, patents, and/or open-source codes
Strong background in physics and coding, and passion for working on and understanding physics-based devices
Atomistic simulation experience, including one or more of: machine-learned interatomic potentials, large biomolecules, reaction kinetics, polymer physics, or PEM fuel cells
Experience applying machine learning or artificial intelligence to atomistic physics simulations