Postdoctoral Scholar — AI Researcher for Critical Mineral Discovery

Benefits

Parental leave

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Non-birth-parent leave

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Family-building benefits

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• Adoption assistance: Not verified
• Surrogacy assistance: Not verified

Mental health support

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Relocation assistance

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Childcare support

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Learning budget

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Salary

$80K-$90K From the posting source checked Jun 20, 2026

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Market context

U.S. role benchmark (BLS OEWS)

$111,944 U.S. median for this role

Projected growth (BLS Employment Projections)

+13.7% - Much faster than average

U.S. benchmark only; posted salary is not compared across countries or currencies.

Matched to SOC 15-1252 - Data and ML aggregate by role bucket.

Source: U.S. Bureau of Labor Statistics, OEWS, May 2024 and Employment Projections, 2024-2034.

Role

Role function

Data From the posting source checked Jun 20, 2026

Seniority

Mid From the posting source checked Jun 20, 2026

Work mode

Remote From the posting source checked Jun 20, 2026

In-office days

0 days From the posting source checked Jun 20, 2026

Schedule

Shift type

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Weekend work

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Application

Cover letter

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Assessment

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Deadline

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Where they hire

State eligibility is not yet verified.

About this role

Postdoctoral Scholar — AI Researcher for Critical Mineral Discovery Remote Position: 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. Research Scope - Multi-physics inversion: Develop stochastic and ensemble inversion frameworks that jointly assimilate muon flux, seismic (active-source, passive, DAS), magnetics, gravity, and EM data into 3D subsurface property models with calibrated uncertainty. - Muon tomography: Forward modeling, sensor placement optimization, and inversion of cosmic-ray muon attenuation data from borehole and surface detectors to constrain ore body density at depth. - Machine learning for geoscience: Apply deep generative models, geostatistical priors, and physics-informed neural networks to regional-to-deposit-scale targeting and resource estimation. Build pipelines that respect geological process constraints rather than purely data-driven correlations. - Decision under uncertainty: Extend Mineral-X's intelligent agent framework to sequential data acquisition decisions (drill hole placement, geophysical survey design) that maximize information value per dollar. Required Qualifications - 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

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