Accelerate the decarbonization of the electricity grid by designing and testing forecasting models.
Requirements
- Strong foundation in math, probability, statistics, and algorithms.
- Proficiency in Python and familiarity with ML libraries/frameworks (e.g., PyTorch, TensorFlow, scikit-learn, numpy, pandas).
- Good understanding of data structures and software engineering principles.
- Previous internship, research, or project experience in machine learning, forecasting, or time-series modeling.
- Familiarity with energy systems, climate tech, or optimization problems.
- Contributions to open-source ML projects or personal ML research.
Responsibilities
- Assist in designing and implementing machine learning models for electricity grid forecasting.
- Explore and prototype ML algorithms for generative time-series forecasting.
- Support the extension and improvement of existing ML libraries and frameworks.
- Run experiments and analyze results to improve model performance.
- Help monitor and evaluate the performance of production models.
- Contribute to team discussions, brainstorming, and problem-solving.
Other
- Currently pursuing or recently completed a BSc, MSc, or PhD in Computer Science, Electrical Engineering, Mathematics, Statistics, or a related field.
- Strong analytical and problem-solving skills.
- Excellent communication skills and ability to collaborate in a team environment.
