Inria Chile now supports 10 Associate Teams: 4 new Franco-Chilean projects launched this year

As of 2025, a total of 10 Associate Teams are active, connecting Inria research groups in France with research teams in Chile, matching the record number reached in 2022. Four new teams (Art’In Blue, BILENS, DORSAL-IoT, and VALPO) have joined six existing projects, boosting bilateral research and collaboration in digital sciences and technologies, and strengthening Franco-Chilean scientific partnerships in these fields.

These binational projects are built around a shared scientific goal and a collaborative research plan designed to foster mutual scientific exchange. Since Inria Chile was founded, 37 Associate Teams have been created and funded, engaging dozens of senior and early career researchers and generating synergies between research groups in both countries.

The new teams, Art’In Blue, BILENS, DORSAL-IoT, and VALPO, join aStoNiched, FLOTTE, FUSION, GRAPA, PANDA, and SWAM in 2025, benefiting from this program which funds researcher mobility within the framework of a three year bilateral research project

The Associate Teams Program

An Associate Team is a joint research project between an Inria project-team and a research team based abroad. Over a period of three years, the partners jointly define a scientific objective, a research plan, and a program of bilateral exchange.

Since Inria's arrival in Chile in 2012, 37 Franco-Chilean research projects in diverse areas of digital science and technology have been funded through this program.

Currently, ten Associate Teams are active, involving researchers from several Inria centers in France—such as the Inria Centre at Université de Bordeaux, Inria Centre at Université de Lille, Inria Centre at Université de Lorraine, Inria Centre at Université de Rennes, Inria antenna at Université de Montpellier, and Inria Centre at Université Côte d’Azur—and from Chilean institutions, including the Universidad de Chile, Pontificia Universidad Católica de Chile, Universidad de Valparaíso, Universidad Técnica Federico Santa María, Universidad Austral de Chile, Universidad de Santiago, Universidad Adolfo Ibáñez, Universidad de O’Higgins, Pontificia Universidad Católica de Valparaíso and the Universidad de Concepción.

Discover our new 2025 Associate Teams:

Art’In Blue: Artificial Intelligence to Understand and Manage Microbial Ecosystems

Led by Olivier Bernard, GREENOWL project-team, Inria Centre at Université Côte d'Azur and David Jeison, researcher Pontificia Universidad Católica de Valparaíso, with collaborators from Inria Chile and Modela CFD.

This project focuses on developing advanced models for microbial ecosystems driven mainly by the activity of microalgae and bacteria in nitrogen, phosphorus, and carbon cycles. The aim is to accurately model these complex, nonlinear systems, applying knowledge from wastewater treatment to marine ecosystems and groundwater denitrification processes. The project leverages AI technologies and large monitoring datasets to improve model representation and system control.

Learn more about Art'In Blue: click here

BILENS – Bilevel Optimization for Logistics, Energy, and Security

Coordinated by Luce Brotcorne, INOCS project-team, Inria Centre at Université de Lille and Alejandro Jofré, researcher Center for Mathematical Modeling University of Chile, BILENS also involves researchers from the Institute for Complex Engineering Systems (ISCI) and Federico Santa María Technical University.

BILENS develops new models and solution methods for bilevel programming problems and Stackelberg games. Bilevel problems involve nested optimization tasks, while Stackelberg games model a leader-follower dynamic where decisions are made sequentially. The team explores these frameworks in logistics, energy, and security, in collaboration with industry, to develop impactful and practical applications.

Learn more about BILENS: Click here

DORSAL-IoT: Downlink Optimization for Robust Direct to Satellite Communication in the Internet of Things

This project is led by Oana Iova, AGORA project-team, Inria Centre at Lyon and Cesar Azurdia, University of Chile researcher, with collaborators from their respective institutions and Pontificia Universidad Católica de Chile.

The Direct to Satellite Internet of Things (DtS-IoT) paradigm merges LPWAN terrestrial networks with LEO satellites to enable global data transmission for IoT devices, supporting applications like asset tracking and remote agriculture. However, implementation is challenged by satellite footprint shifts, long distances, channel dynamics, and device limitations. This project investigates downlink techniques in DtS-IoT systems based on LoRaWAN, a viable connectivity technology, using optimization and machine learning to orchestrate downlink messaging, avoid collisions, and ensure efficient and robust communication between remote IoT devices and satellite networks.

Learn more about DORSAL-IoT: Click here

VALPO: Statistical Validation of Longitudinal, Compositional, and Large Scale Microbiome Data Analysis to Predict Health Outcomes

Led by Marta Avalos-Fernandez, SISTM project-team Inria Centre at Université de Bordeaux and Cristian Meza, Valparaíso Research Center for the Modeling of Random Phenomena (CIMFAV), University of Valparaíso, this Associate Team includes collaborators from Inria Chile, Adolfo Ibáñez University, Centre Hospitalier Universitaire (CHU) Bordeaux Pellegrin/Inserm, and the PLEIADE project-team Inria Centre at Université de Bordeaux.

The project develops statistical methods to analyze complex microbiome data particularly longitudinal, compositional, and high-dimensional datasets that are sparse, zero-inflated, and temporally dependent. The research will expand methodologies to visualize microbiome time series, identify microbial features linked to disease using SAEM based approaches, and predict health outcomes through machine learning techniques. The collaboration combines Chilean and French expertise in statistics, with applications in chronic disease and vaccine response, and aims to explore further open access data use cases.

Learn more about VALPO: Click Here