GE Vernova Time-Series Foundation Models Research Intern - Summer 2026 🎓

As a GE Vernova accelerator, GE Vernova Advanced Research is driving strategy and leading research & development efforts to execute on the business’s mission to help power the energy transition. We forge the collaborations and help invent the technologies required to electrify and decarbonize for a zero-carbon future.

Representing virtually every major scientific and engineering discipline, our researchers are collaborating with GE Vernova’s businesses, the U.S. government, and more than 420 entities at the forefront of technology to execute on 150+ energy focused projects. Collectively, these research programs and initiatives aim to solve near term technical challenges, deliver next generation product advances, and drive long term breakthrough innovation to enable more affordable, reliable, sustainable, and secure energy.

Come and join our powerful, unified force with the energy to change the world. Our mission is BIG.

Our TRANSFORMATION is key – bringing the right businesses together to LEAD the ENERGY TRANSITION. Our TEAM is ready.

Addressing the climate crisis is an urgent global priority and we take our responsibility seriously. Building on over 130 years of experience tackling the world’s challenges, GE Vernova is uniquely positioned to help lead the energy transition by continuing to electrify the world while simultaneously working to decarbonize it. GE Vernova helps customers power economies and deliver electricity that is vital to health, safety, security, and improved quality of life.

Foundation models for time-series data are general-purpose models pretrained on large-scale, diverse time-series datasets collected from various applications and domains. These models are capable of performing a wide range of time-series tasks, such as forecasting, classification, and anomaly detection. Similar to large language models (LLMs), time-series foundation models (TSFMs) are task- and application-agnostic, meaning that they can be trained and deployed at scale across different use cases.

We are looking for a graduate student with strong background on AI/ML and knowledge and experience working with time-series data, to research, develop, and test TSFMs for energy systems, such as gas turbines, wind turbines and power grid. This includes research on transformer-based architectures, model training and validation, and hyperparameter optimization over multiple GPUs.