Scientific Machine Learning Modeller

Rainmaker

El Segundo, CA, United States

Posted on: 2025-02-06

Category: sustainability

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Expired

Employment type:

Full time

Experience required:

Intermediate

Salary

Salary not provided

About the company:

Rainmaker provides water for farms, watersheds, and ecosystems, fortifying growth and stewarding the natural world. We are Making Earth Habitable.

We are seeking a highly motivated and skilled Scientific Machine Learning Specialist to lead the development and implementation of advanced machine learning models aimed at validating the efficacy of precipitation enhancement operations. The successful candidate will work at the intersection of atmospheric science, data analytics, and artificial intelligence to address challenges in weather prediction and precipitation enhancement.

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What You'll Do

Design, train, and validate machine learning models to analyze meteorological and observational data related to precipitation enhancement.

Develop algorithms to detect and quantify the impact of precipitation enhancement on precipitation patterns and amounts using large-scale atmospheric datasets.

Work with large-scale atmospheric datasets, including satellite, radar, and reanalysis data.

Collaborate with atmospheric scientists, physicists, and AI researchers to integrate domain knowledge into machine learning frameworks.

Develop scalable software solutions for cloud-resolving and weather prediction models.

Qualifications

Masters or PhD in atmospheric science, meteorology, physics, applied mathematics, computer science, or related discipline.

Strong background in machine learning (deep learning, neural networks, etc.) and numerical methods for the predication of weather.

Experience with physics-informed neural networks (PINNs) or hybrid AI-physics modeling.

Proficiency in Python and deep learning frameworks (e.g., TensorFlow, PyTorch).

Knowledge of fluid dynamics, thermodynamics, cloud microphysics, and precipitation processes

Rainmaker

30

Full time

Rainmaker

30

Full time

Sustainability