Role Overview

Title: Reservoir Simulation + ML Research Scientist

Hours: Full time

Location: Salt Lake City, UT (preferred) or global remote

Full-Time Employment for Onsite

Remote Possible for Contractors

Benefits Eligible: Yes

Manager: Ahinoam Pollack, Head of Research

Mission – Why we exist and why we need you

Geothermal energy is the most abundant renewable energy source in the world. There is 2,300 times more energy in geothermal heat in the ground than in oil, gas, coal, and methane combined. However, historically it’s been hard to find and expensive to develop. At Zanskar, we’re using better technology to find and develop new geothermal resources in order to make geothermal a cheap and vital contributor to a carbon-free electrical grid. Zanskar has raised >$15M from top-tier VC firms (Lowercarbon, USV, Munich Re Ventures, etc.) and is planning for significant growth over the next 12 months.

The research team at Zanskar is at the cutting edge of developing tools to pinpoint and characterize geothermal resources with higher precision than ever before. To achieve this capability, we rely on both physical and machine learning modeling. We need a scientist with double expertise in both flow simulation (reservoir engineering) and optimization or machine learning. This key player on our team will help us build new ML + optimization-based workflows for temperature data assimilation, as well as optimize field development plans. This modeling position is crucial for advancing geothermal development technology to the next level and enabling Zanskar to achieve its mission.

Outcomes - Problems you’ll solve

Success in this role will look like patentable workflows and tools that our team will deploy in the field in the next year to find and de-risk an unprecedented number of geothermal resources that can be developed into commercial power plants before 2030. The workflows would automatically assimilate pressure, temperature and flow data to reduce the subsurface uncertainty of the geothermal resources. In addition, the role’s work will include assessing the value of information and optimizing well placement in the field.

Competencies – What we’re looking for

- Flow simulation (reservoir engineering): The candidate should have over three years of experience running flow simulations in commercial or academic software for field applications such as geothermal (preferred), carbon capture and storage, water treatments, or oil and gas.

- Machine learning: The candidate should have experience developing and deploying machine learning models. Preferably, training ML models for predicting properties on large 2D/3D models, using methods such as CNN, GNN, PINN, GANs, or similar. Extra bonus points for experience in training emulators for flow simulators.

- Effective communication skills: Collaborate with cross-functional teams, including exploration geologists, data science, and business leaders. This includes communicating results, roadblocks, clarifications, and recommendations for next steps and road maps.

- This is a Senior or Staff level position. To be competitive, a candidate will most likely need a Master’s & 3yrs+ experience or a PhD & 1yr+ experience.