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2024 PhD Residency - ML / Physics - Photonic Inverse Design

🚀 Off-cycle Internship

Mountain View

⌛ Closed
Applications are closed

Off-cycle Internship

Data•Mountain View


During Your Internship You Can Expect:

  • To be placed on one of our confidential or public X projects
  • To get paid competitively and with Google benefits
  • To be part of a lively community of other Interns and Residents 
  • To addend colloquium and discussions with team leads from across Google, DeepMind and external organizations

About the Project / Team:

  • We are exploring automated design for electromagnetic devices. This capability could enable applications ranging from integrated photonics to metasurfaces, and requires technical developments in ultra-large-scale simulators, novel optimization schemes, and prototyping / fabrication capabilities. This effort is focused on inventing fundamentally new technologies with the ambition of bringing disruptive products to market.


  • 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

Education requirements

Currently Studying

Area of 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


Work type

Full time

Work mode



Mountain View