I’m an AI Research Scientist specializing in Deep Learning for time series and temporal data. My work focuses on designing neural architectures capable of learning from complex temporal dynamics — from forecasting to representation learning.
Recently, my research has been centered on developing foundation models for time series, aiming to build general-purpose models that can transfer across domains and tasks in a zero-shot manner.

• Deep learning for time series and spatio-temporal modeling
• Foundation models, large-scale pretraining and inference adaptation for time series
• Time series representation learning and self-supervised learning
• Imputation, forecasting, and other supervised tasks
• Scientific and industrial applications of AI

You can explore my main research works, open-source projects, and articles on my personal website:
https://etiennelnr.github.io

If you're interested in collaborating, discussing research, or simply exchanging ideas about AI, feel free to reach out through the contact links on my website.