Following the close of the EIC Pathfinder Open on May 12th, 2026, the EIC Pathfinder Challenges calls are now being launched with an indicative total budget of 96 million euros.

Three challenges have been published on July 22nd and are open for submission until October 28th, 2026.

The first challenge: Advanced Materials for Miniaturized Energy Harvesting Systems

The objective of this call is to support the development of a new generation of energy-autonomous systems that enable innovative services to improve the quality of life for European citizens through applications in areas such as diagnostic tools for patients and sustainable, smart cities.

The EIC will fund highly ambitious research projects aimed at developing new materials that enable small connected devices (sensors, wearables, medical devices, etc.) to harvest energy from their environment and operate autonomously. The goal is to achieve a laboratory proof of concept (TRL 4), while identifying potential markets from the outset (ideally with letters of support from prospective customers already in hand).

These advances are expected to support sustainability in both energy consumption and production, in line with the ambitions of RePowerEU and the European Green Deal. Furthermore, this challenge will improve the overall sustainability of the Internet of Things (IoT) and energy-autonomous systems in general

The second challenge: Biotechnology for Healthy Ageing

The EIC will fund ambitious proposals that are expected to deliver the following results:

• Proof-of-concepts (TRL3 completed) of biotechnology-based or pharmaceutical interventions that prevents or delays the onset of, or reverts, an age-related disease in a vertebrate model system, based on the hallmarks of ageing, taking into consideration practical challenges of implementing such an intervention.
• Tools to facilitate development or adoption of the interventions above, such as proof- of-concept validation of biomarker signatures or suitable pre-clinical models, and
• Approaches to address the shared regulatory hurdles and societal challenges linked to ageing-related interventions, thereby facilitating their adoption.

Applicants to this Challenge will be expected to develop a proof of concept in one of the following three areas:

1. An innovative preventative or therapeutic biotechnology-based orpharmaceutical intervention that prevents, delays or reverts the onset of a specificage-related disease.
2. A biomarker-based tool to enable the responsible deployment of ageing-related Interventions.

Biomarkers are biological characteristics, which can be molecular, anatomic, physiologic, or biochemical. These characteristics can be measured and evaluated objectively. They act as indicators of a normal or a pathogenic biological process. They allow the assessment of the pharmacological response to a therapeutic intervention. A biomarker shows a specific physical trait or a measurable biologically produced change in the body that is linked to a disease or a particular health condition. A biomarker may be used to assess or detect a specific disease as early as possible (diagnostic biomarker), the risk of developing a disease (susceptibility/risk biomarker), the evolution of a disease (prognostic biomarker) – but it can also predict response to a given treatment including potential toxicity (predictive biomarker)

3. A New Approach Methodology (NAM) that goes beyond the current state-of-the artto enable the future development of interventions for healthy ageing.

New approach methodologies (NAMs) represent potential alternatives to animal testing in the non-clinical development phase of new medicines. They may include human organoids or microphysiological systems (e.g. organ-on-chip, disease-on-chip), in chemico methods, digital twins, virtual patient simulations, AI-enhanced predictive models, mechanistic or integrated in silico platforms, 3D- advanced human tissue model.

The third challenge : DeepRAP: Deep Reasoning, Abstraction & Planning towards trustworthy Cognitive AI Systems

Inspired by the human brain’s ability to process information at multiple levels of abstraction— enabling perception, reasoning, and goal-directed planning—the goal of this Challenge is to move beyond the current state-of-the-art in traditional AI approaches, whether symbolic (e.g., rules, decision trees, symbolic regression, etc.) or connectionist, neural (e.g., deep learning, large language models, reinforcement learning). The goal is to significantly improve the Reasoning, Abstraction, and Planning (RAP) capabilities of AI systems.

The proposals should address one or more of the following cognitive capabilities:

1. Deep Reasoning: Moving beyond statistical pattern matching to support causal inference, logical reasoning, and context-aware or commonsense decision-making in complex, unstructured environments.
2. Deep Abstraction: Enabling AI systems to generalise insights from limited data by forming, manipulating, and refining high-level concepts, analogies, and representations that can be transferred across diverse application domains.
3. Deep Planning: Developing robust, adaptive, and scalable planning algorithms/model capable of operating in open-world, agentic, or uncertain real-time environments.

Author: Camille MAISSE