Machine Learning Research Intern

Rückbauanlage Gundremmingen • Austin, TX

1d

About The Position

The Machine Learning Research Intern will work at the intersection of atmospheric science, renewable energy, and advanced AI. This role focuses on developing diffusion models for mesoscale downscaling, ML-driven data-cleaning systems, and supervised learning approaches to quantify turbine performance losses from atmospheric conditions. You will work on real operational datasets and contribute research that transitions into production systems used across large wind portfolios.

Requirements

• Working on a degree in Business, Information Science/Technology, and a strong interest in renewables
• Strong Python skills (TensorFlow/Keras, NumPy, Pandas, xarray)
• Exposure to diffusion or generative models
• Excellent grasp of object-oriented programming
• Strong GitHub workflow experience
• Comfort working with messy, real-world datasets
• Strong written and verbal communication
• Team-oriented, curious, and self-drive
• Applicants must be legally authorized to work in the United States.
• RWE Clean Energy is unable to sponsor or take over sponsorship of employment visas at this time.

Nice To Haves

• Atmospheric science, physics, or energy systems background
• Experience with mesoscale or reanalysis models (WRF, ERA5)
• Knowledge of uncertainty quantification or physics-informed ML
• Experience moving research into operational pipelines

Responsibilities

• Develop diffusion-based generative models for high-resolution wind field reconstruction and downscaling
• Build supervised and unsupervised ML pipelines for cleaning meteorological time-series and metadata
• Create supervised learning models to predict power-performance losses from atmospheric variables
• Write clean, object-oriented Python code using TensorFlow/Keras and scientific libraries
• Collaborate through GitHub with structured PRs, reviews, and version control
• Work directly with domain experts and data owners to acquire and understand raw datasets
• Use LLM tools productively for coding and debugging while remaining technically independent

Benefits

• Paid time off
• Holidays

What This Job Offers

• Job Type: Full-time
• Career Level: Intern
• Education Level: No Education Listed