Objectives

Call EDF-2026-RA targets Research actions, funded through Actual costs grants following the call for proposals, and is open to any eligible consortium as defined in Articles 9 and 10(4) of the EDF Regulation.  The call topics cover: CBRN decontamination systems and technologies, multidomain sensors demonstrator and test, quantum secured tactical networks, autonomous and automatic air-to-air refuelling, future multirole light aircraft, development and validation of models predicting flow-related underwater noise, and a modelling & simulation-supported AI framework for military decision-making and training.

Across topics, the call seeks to generate and integrate knowledge, carry out studies and design activities, develop and validate models and solutions, and demonstrate feasibility of new capabilities that improve operational effectiveness and resilience, while contributing to reducing or preventing the strategic dependencies of the Union. In particular, it addresses improved CBRN decontamination processes, enhanced sensing and testing approaches for multi-domain environments, secure tactical networking based on quantum technologies leveraging the technological sovereignty of Europe, increased effectiveness and automation in air operations through air-to-air refuelling, and improved understanding and prediction of underwater noise. It also aims to assess the feasibility of creating an AI framework for defence through AI-enabled battlespace simulation, supporting military decision-making and training.

Actions

This call covers the following topics:

EDF-2026-RA-SIMTRAIN-MSAI: Modelling & simulation supported AI framework for military decision-making and training

EDF-2026-RA-SENS-MSDT: Multidomain sensors demonstrator and test

EDF-2026-RA-CYBER-QSTN: Quantum secured tactical networks

EDF-2026-RA-MCBRN-DST: CBRN decontamination systems and technologies

EDF-2026-RA-PROTMOB-FMLA: Future multirole light aircraft

EDF-2026-RA-AIR-A4R: Autonomous and automatic air-to-air refuelling

EDF-2026-RA-UWW-FUWN: Development and validation of models predicting flow-related underwater noise