Phd Research Intern, AI for Climate and Weather Simulation 2026

NVIDIA NVIDIA · Semiconductors · United Kingdom · Remote

NVIDIA is seeking a PhD Research Intern to apply modern AI methods to climate and weather simulation. The role involves proposing, researching, and prototyping innovative ideas, publishing groundbreaking work, and contributing to technology transfer. The intern will utilize NVIDIA GPUs for cutting-edge research at the intersection of AI and climate science.

What you'd actually do

  1. Propose, research, prototype and test innovative research ideas.
  2. Publish groundbreaking work at top conferences and journals.
  3. Collaborate with other research team members, fellow interns, internal product teams, external researchers and be mentored.
  4. Contribute to technology transfer with engineers around NVIDIA as ideas graduate from research to product.
  5. Make good use of top-of-the-line NVIDIA GPUs at scale for cutting edge research at the intersection of AI and climate science.

Skills

Required

  • Currently enrolled in at least the 2nd year of a Ph.D. in the geophysical sciences, computer science, applied math/statistics, or related fields.
  • Strong research portfolio including one first-author publication that makes good use of AI.
  • Proficiency or demonstrable ability to quickly absorb distributed deep learning training frameworks, e.g., PyTorch.
  • Strong software engineering skills are necessary.
  • Excellent communication skills.

Nice to have

  • Experience in scaling algorithms for high computational loads is a plus.
  • Experience developing in a changing software environment and ability to drive research projects end-to-end -- including the messy parts – are a plus.
  • Expertise in climate domain science, nonlinear physics, or deep familiarity with associated synthetic and/or observational datasets and/or physical simulation systems are a plus.

What the JD emphasized

  • strong research portfolio including one first-author publication that makes good use of AI

Other signals

  • AI for climate and weather simulation
  • generative data assimilation
  • hybrid climate simulation
  • full model emulation
  • AI autoregressive weather prediction
  • dynamical downscaling