Physics-Informed AI Intern
About The Position
Keysight is at 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 award-winning culture embraces a bold vision of where technology can take us and a passion for tackling challenging problems with industry-first solutions. We believe that when people feel a sense of belonging, they can be more creative, innovative, and thrive at all points in their careers.
Requirements
- Pursuing PhD in EE, Applied Math, CS, or related field.
- Strong hands-on experience with GNNs, Transformers, Vision Models, and generative models.
- Background in Bayesian/numerical optimization and applied RL.
- Proficiency in Python, C++, CUDA; experience with distributed/HPC training.
- Solid software engineering fundamentals (testing, CI/CD, modular design).
- Candidates who wish to be considered must be enrolled in a accredited college/university as of September 2026. Applicants who have graduated before September 2026 will not be considered unless they are entering/applying to a MS or PHD program after graduating.
- Visa Sponsorship is not available for this position. Candidates who now or at any point in the future require sponsorship for employment visa status (e.g., H-1B Visa status) may not be considered.
Nice To Haves
- Experience applying ML/RL to physical parameter tuning or design exploration.
- Familiarity with Keysight tools (ADS, RFPro, EMPro, Signal Studio).
- Publications or patents in scientific ML, generative modeling, RL, or optimization.
Responsibilities
- Formulate physics-informed ML problems in collaboration with RF, EM, circuit, and CAE domain experts.
- Implement PINNs (embedding PDEs as soft/hard constraints), Neural Operators (FNO, DeepONet, GNO) for EM/S-parameter surrogate modeling, and hybrid physics-data models.
- Build fast ML surrogates for CAE workflows — replacing or accelerating FEM, FDTD, and MoM solvers for thermal, structural, and electromagnetic simulation in the design loop.
- Develop GNN-based models for topology-aware physical circuit and transmission line modeling.
- Apply physics-constrained Bayesian optimization, adjoint/gradient methods for differentiable simulators, and RL with physics-based reward shaping.
- Develop scalable pipelines with physics-aware data loaders and benchmark against full-wave EM and CAE reference solvers.
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What This Job Offers
Career Level
Intern
Education Level
Ph.D. or professional degree
