Internship, Ph.D., Postdocs, and Research Engineer positions

Feel free to contact me (paul.boniol@inria.fr) if you are interested in one of the positions below.

🔬 Research Internship Position at Inria/ENS Paris

Role: Master’s Research Intern – Time Series Interpretable Analytics

Location: École Normale Supérieure (ENS), Paris, France
Host Institution: Inria Paris & ENS-PSL (VALDA Team)
Duration: 6 months (Flexible, targeting February–July 2026)
Project: Interpretable Segmentation Framework for Time Series

Position Overview

We are seeking a highly motivated Master’s Research Intern to contribute to cutting-edge research in Interpretable Machine Learning for Time Series Analytics. You will be part of the VALDA team at Inria/ENS Paris, focusing on reconciling predictive performance with model transparency in complex temporal data.

This internship is a hands-on project aimed at designing and prototyping a novel framework for interpretable time series segmentation. The work involves leveraging hierarchical graph-based structures (inspired by HNSW) to build a multi-scale representation of time series data.

Key Objectives & Responsibilities

Framework Design: Design the hierarchical aggregation mechanism for constructing a multi-scale, nearest-neighbor graph representation of time series subsequences.
Prototyping & Implementation: Implement the segmentation framework using Python, utilizing scientific libraries like NumPy and PyTorch.
Evaluation: Rigorously evaluate the method using specialized interpretable segmentation measures, quantifying the inherent trade-off between explanatory power and segmentation accuracy.
Documentation: Maintain high code quality, comprehensive documentation, and contribute to scientific communication.

Candidate Profile

Education:
- Master’s student (M2 or equivalent) in Computer Science, Applied Mathematics, Data Science, or a related technical field.
Technical Skills:
- Strong programming proficiency in Python.
- Experience with core scientific libraries (NumPy, PyTorch, scikit-learn).
- Solid theoretical background in Machine Learning, Algorithms, and Statistics.
- Personal Attributes: Proven autonomy, scientific curiosity, and excellent communication skills (English required; French is a plus).

Start Date

Flexible
targeting February 2026.

Application Process

Please send a detailed CV and a brief Statement of Interest to:

Félix Chavelli: felix.chavelli@inria.fr
Michael Thomazo: michael.thomazo@inria.fr
Paul Boniol: paul.boniol@inria.fr