Postdoctoral Scholar – AI Researcher, Critical Mineral Discovery

Job not on LinkedIn

🕒 June 3

🏄 California – Remote

🏄 California – Remote

💵 $80k - $90k / year

⏰ Full Time

🟡 Mid-level

🟠 Senior

🧠 AI Research Scientist

👻 Ghost score 21%

What it means: how likely this posting is sitting open without active hiring.How to read it: 60% or higher means we think this is a ghost. Lower scores are less of a worry.How we score it: age, reposts, open roles vs team size, how fast jobs close, and whether pay is listed.Fine print: an estimate from public data, not a judgment of the company. Real jobs can score high, so treat it as a hint, not a fact.

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KoBold Metals

WebsiteLinkedInAll Job Openings

51 - 200 employees

Founded 2019

🤖 Artificial Intelligence

🔬 Science

⚡ Energy

Artificial Intelligence • Science • Energy

KoBold Metals is a company specializing in advanced mineral exploration. By investing over $100 million annually across 60+ projects on five continents, KoBold is at the forefront of using artificial intelligence and novel hardware to transform mineral exploration into a repeatable science. The company aggregates comprehensive data on the Earth's crust and utilizes proprietary AI tools, including the Machine Prospector® system, to minimize uncertainties in exploration. KoBold Metals is committed to ethical exploration practices and is backed by world-class investors dedicated to discovering and developing new battery metal resources. Their innovative approach leverages AI to build the world's largest geoscience information collection and model subsurface data statistically.

📋 Description

• Develop and apply AI, muon tomography, seismic imaging, and geophysical inversion methods to discover and characterize copper, nickel, lithium, cobalt, and rare earth deposits. • Develop stochastic and ensemble inversion frameworks that assimilate muon flux, seismic, magnetics, gravity, and EM data into 3D subsurface property models. • Forward modeling, sensor placement optimization, and inversion of cosmic-ray muon attenuation data. • Apply deep generative models, geostatistical priors, and physics-informed neural networks for resource estimation. • Extend Mineral-X’s intelligent agent framework for data acquisition decisions.

🎯 Requirements

• PhD (fully completed, and within 4 years of graduation) in physics, applied physics, geophysics, computational earth sciences, machine learning, applied mathematics, or related field. • Strong Python proficiency (NumPy, PyTorch or JAX, scientific stack); experience with HPC, GPU computing, and reproducible research workflows. • Track record of publishing in peer-reviewed venues and willingness to engage directly with industry collaborators and field data.

🏖️ Benefits

• Competitive Stanford postdoctoral salary • Medical • Dental • Vision • Conference travel support