Scientific Machine Learning Intern
Keysight Technologies | |
United States, California, Santa Rosa | |
1400 Fountaingrove Parkway (Show on map) | |
Jan 09, 2025 | |
Overview
Keysight is on the forefront of technology innovation, delivering breakthroughs and trusted insights in electronic design, simulation, prototyping, test, manufacturing, and optimization. Our ~15,000 employees create world-class solutions in communications, 5G, automotive, energy, quantum, aerospace, defense, and semiconductor markets for customers in over 100 countries. Learn more about what we do. Our powerful, award-winning culture embraces a bold vision of where technology can take us and a passion for tackling challenging problems with industry-first solutions. Diversity, equity & inclusion are integral parts of our culture and drivers of innovation at Keysight. We believe that when people feel a sense of belonging, they can be more creative, innovative, and thrive at all points in their careers. Responsibilities We are seeking a highly motivated and qualified individual to join our team as a Machine Learning R&D (SciML) Intern. This role involves cutting-edge research and development in machine learning within the testing and measurement industry, with a focus on enabling next-generation simulation and design. The successful candidate will develop and refine neural surrogate models for circuit, electromagnetic, and electrothermal simulations while implementing advanced techniques such as transformers, neural operators, mamba, and graph neural networks. They will design and test machine learning learning frameworks for complex simulation tasks and ensure the accuracy and reliability of AI models in scientific and engineering applications. This role requires working closely with cross-functional teams to seamlessly integrate AI and ML solutions into Keysight's products and business operations. The position demands a proactive and self-motivated individual who thrives in dynamic environments, quickly building relationships and adapting to change and ambiguity. Strong programming skills and experience in developing, prototyping, and delivering sophisticated algorithmic solutions are essential. The ideal candidate will have a strong academic and practical background in deep learning, programming, and applied AI techniques. Hands-on experience with hardware-in-the-loop systems or physical simulation environments is highly desirable. A passion for interdisciplinary collaboration, integrating machine learning with engineering and testing, and solving real-world challenges is essential for success in this role Qualifications Careers Privacy Statement
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