Must be enrolled in an academic program and working towards completing a PhD
Minimum Qualifications:
Currently pursuing an MS or PhD in physics, engineering, computer science, mathematics, or a related field
Solid Python coding skills with an emphasis on software development best practices including code organization, testing, and readability
Experience with the Python numerical and scientific computing stack (NumPy, SciPy, Pandas, etc.)
Preferred Qualifications:
Past work involving differentiable physics simulators, adjoint methods, “physics for machine learning,” or “machine learning for physics”
Research experience using modern machine learning libraries such as JAX, TensorFlow, or PyTorch
Research experience with numerical methods for solving ordinary and partial differential equations
Experience with applying constrained optimization techniques and algorithms (e.g. global optimization, local optimization, combinatorial optimization), especially in the domain of topology and shape optimization
Experience with using or developing computational electromagnetic simulators (FDTD, FDFD, FEM, RCWA, etc.)
Experience developing surrogate models for applications in physics and scientific computing
Demonstrated contributions to open source projects in the area of scientific computing
Responsibilities
Research and develop constrained optimization strategies and machine learning techniques for automating the design of photonic devices
Explore and implement novel simulation techniques for electromagnetic and optical devices
Develop data-driven and physics-based models for semiconductor device foundry fabrication processes