We are seeking a motivated Machine Learning Intern to help design and test forecasting models that accelerate the decarbonization of the electricity grid. This role is ideal for students or recent graduates who want to apply their programming and analytical skills in a fast-paced environment, learn from experienced ML engineers, and contribute to solving real-world challenges in energy and climate. You will have the opportunity to work on cutting-edge problems in generative time-series forecasting, collaborate with a team of talented engineers and researchers, and see your ideas tested in real-world applications.
Requirements
- Currently pursuing or recently completed a BSc, MSc, or PhD in Computer Science, Electrical Engineering, Mathematics, Statistics, or a related field.
- 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.
- Strong analytical and problem-solving skills.
- Excellent communication skills and ability to collaborate in a team environment.
Nice to Have
- 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.
What You’ll Gain
- Hands-on experience developing ML models with direct impact on renewable energy integration.
- Mentorship from experienced ML engineers and researchers.
- Exposure to cutting-edge methods in generative forecasting and grid decarbonization.
- Opportunity to contribute to meaningful, climate-focused innovation.
Responsibilites:
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.
Requirements:
Currently pursuing or recently completed a BSc, MSc, or PhD in Computer Science, Electrical Engineering, Mathematics, Statistics, or a related field.
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.
Strong analytical and problem-solving skills.
Excellent communication skills and ability to collaborate in a team environment.
Nice to Haves:
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.
What You'll Gain:
Hands-on experience developing ML models with direct impact on renewable energy integration.
Mentorship from experienced ML engineers and researchers.
Exposure to cutting-edge methods in generative forecasting and grid decarbonization.
Opportunity to contribute to meaningful, climate-focused innovation.
Estas cookies son necesarias para que el sitio web funcione y no se pueden desactivar en nuestros sistemas. Puede configurar su navegador para bloquear estas cookies, pero entonces algunas partes del sitio web podrían no funcionar.
Seguridad
Experiencia de usuario
Cookies orientadas al público objetivo
Estas cookies son instaladas a través de nuestro sitio web por nuestros socios publicitarios. Estas empresas pueden utilizarlas para elaborar un perfil de sus intereses y mostrarle publicidad relevante en otros lugares.
Google Analytics
Anuncios Google
Utilizamos cookies
🍪
Nuestro sitio web utiliza cookies y tecnologías similares para personalizar el contenido, optimizar la experiencia del usuario e indvidualizar y evaluar la publicidad. Al hacer clic en Aceptar o activar una opción en la configuración de cookies, usted acepta esto.
Los mejores empleos remotos por correo electrónico
¡Únete a más de 5.000 personas que reciben alertas semanales con empleos remotos!