Development Economist Machine Learning Expert

This is a remote, project-based role for machine learning researchers and engineers with deep expertise in ML applied to geologic modeling and earth science. You will complete tasks at the intersection of machine learning and geoscience — including model development, subsurface characterization, and research tasks applied to seismic interpretation, stratigraphic modeling, mineral exploration, and geophysical inversion. Work is over the next 2–3 weeks, asynchronous, and assigned on a project-by-project basis, with an expected commitment of 10–20 hours per week for the projects you accept. This position offers exceptional pay, exposure to cutting-edge geoscience ML research, and a strong addition to your research portfolio.

Responsibilities

• Apply machine learning techniques to geologic modeling tasks including seismic interpretation, facies classification, subsurface property prediction, and geophysical inversion
• Build and evaluate ML models trained on well log, seismic, remote sensing, and geochemical datasets
• Develop generative and predictive models for subsurface uncertainty quantification, stratigraphic forward modeling, and basin analysis
• Integrate ML approaches with physics-based geologic simulators and geostatistical workflows
• Document methodologies, experimental results, and technical approaches clearly and reproducibly

Required qualifications

• Published researcher with at least one first-author publication in a peer-reviewed venue (e.g., NeurIPS, ICML, Geophysics, Journal of Geophysical Research, Basin Research, or equivalent)
• Master's or PhD in Geoscience, Geophysics, Geology, Earth Science, Computer Science, or a related quantitative field
• Demonstrated expertise in both machine learning and geologic or geophysical modeling
• Strong problem-solving skills and ability to work independently on technical and research tasks

Preferred qualifications

• Hands-on experience with geoscience tools and datasets (e.g., Petrel, OpendTect, SEGY seismic data, well log databases, or similar)
• Familiarity with ML applications in geoscience (e.g., seismic facies classification, well log prediction, generative geological modeling, or geophysical inversion networks)
• Experience with geostatistical methods, uncertainty quantification, or physics-informed neural networks applied to subsurface systems
• Background in TA'ing or teaching geoscience, geophysics, or machine learning courses