$ cat jobs/semiconductor-device-modeling-engineer-dodmg-22d14d0867e5.json
Semiconductor Device Modeling Engineer
HRL Laboratories pioneers the next frontiers of physical and information science. Delivering transformative technologies in automotive, aerospace and defense, HRL advances the critical missions of its customers to help them remove limitations and create competitive advantage. HRL then transitions the work back to customers – ready for real-world application. For more than 70 years, HRL’s rich portfolio of scientific discoveries and engineering innovations continues to build on each other — often in unexpected, profound and far-reaching ways. As a private company owned jointly by Boeing and GM, HRL prioritizes purpose over profit, significantly advancing the state of the art. HRL Laboratories advances critical structural and functional materials and semiconductor devices that enable precision navigation, quantum technologies and extreme-environment performance. With expertise from design through deployment, we deliver scalable, high-performance solutions that transform our customers’ missions. Our work leverages digital manufacturing and scalable microfabrication techniques to meet tomorrow’s toughest challenges. HRL Laboratories is looking for a creative and motivated device modeling engineer to join a world-class team of scientists and engineers developing next-generation technologies. In this role, you will push on the frontier of understanding quantum control and transport in semiconductor devices, working in tight feedback loops with fabrication and test efforts. You will utilize both commercial and proprietary device simulation tools to model state-of-the-art electronic technologies like FinFETs and gate-all-around transistors, as well as design and analyze a variety of other classical and quantum nanodevices, including semiconductor spin qubits. Particular areas of focus include understanding semiclassical and quantum transport, electrostatic integrity, and response to device stressors such as heat and strain to predict and optimize performance and interpret e