Physics-Informed AI Intern

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

Our 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.

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.

Required Qualifications

• 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).

Desired Qualifications

• 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.

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.

California Pay Range: $60.82-$65.50 per hour

Based on experience, education and skills, most offers will be between the minimum and the midpoint of the Salary Range listed above.

Note: For other locations, pay ranges will vary by region.

***Keysight is an Equal Opportunity Employer.***