2024-11-12T16:19:17-05:00 X, The Moonshot Factory
PhD Residency - ML / Physics - Photonic inverse design
Internship
Mountain View, CA

How you will make 10x impact: 

This project aims to push the limits of science and modeling as we know them and to prove how ML can radically accelerate our understanding of the world

  • Location: X's headquarters in Mountain View, CA
  • Start Date(s): Year-round rolling basis
  • Duration: a flexible 4 mo. to 1 year program based on project team needs and your availability

Throughout your AI Residency you can expect:

  • To be embedded into one of our confidential or public X projects
  • To get paid competitively and receive benefits
  • To be a part of a lively community of AI and ML Residents
  • To attend tech-talks with AI leaders from across X

Responsibilities: 

  1. Research and develop constrained optimization strategies and machine learning techniques for automating the design of photonic devices
  2. Explore and implement novel simulation techniques for electromagnetic and optical devices
  3. Develop data-driven and physics-based models for semiconductor device foundry fabrication processes

 

About the 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. 

 

What you should have: 

  • 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.)

 

It’d be great if you also had these: 

  • 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

 

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