KoBold Metals
Full TimeDevelop and deploy AI, muon tomography, and geophysical inversion methods to discover critical minerals using real exploration data at Stanford Mineral-X in collaboration with KoBold Metals. This three-year fully funded fellowship requires a PhD in physics, geophysics, machine learning, or related fields completed within four years, with strong Python skills (NumPy, PyTorch/JAX) and HPC experience. Research focuses on multi-physics inversion, machine learning for mineral targeting, and decision-making under uncertainty. Based in Stanford, CA with periodic travel to field sites. Salary $80,000–$90,000.
Written from this posting by Neural Jobs AI. The full description is below.
Fully funded 3-year postdoctoral fellowship at Stanford Mineral-X, advised by Prof. Jef Caers, in collaboration with KoBold Metals, developing and deploying new methods for critical mineral exploration and resource definition. The scholar will develop and apply AI, muon tomography, seismic imaging, and geophysical inversion methods to discover and characterize copper, nickel, lithium, cobalt, and rare earth deposits using real exploration data from active field programs. More information here: https://postdocs.stanford.edu/postdoc-admins/policy/funding-rates-and-guidelines.
Start: Preferably Aug 1, 2026
Salary: $80,000 - $90,000
https://postdocs.stanford.edu/postdoc-admins/policy/funding-rates-and-guidelines
What to Expect
Joining KoBold means getting the opportunity for hands-on exposure to our exploration projects around the world. All employees are expected to travel to project sites, with a minimum of one week per year. Field-facing and technical roles spend significantly more time in the field.
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KoBold Metals is a mineral exploration company using AI to explore for the metals we need for our transition to a low-carbon economy. KoBold’s business is discovering, defining, expanding, and developing mineral resources, and KoBold’s objective is to achieve a step-change improvement in exploration success: we aim to discover more tier 1 resources, faster, and with fewer failures.
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