This article presents the EIC Pathfinder Challenges for 2026, focusing on cutting-edge research in advanced materials, biotechnology, and artificial intelligence. The following challenges have been announced for the European Innovation Council's Pathfinder programme, targeting breakthrough innovations that will shape Europe's technological future.

Table of Contents

  1. Advanced Materials for Miniaturised Energy Harvesting Systems
  2. Biotechnology for Healthy Ageing
  3. DeepRAP: Deep Reasoning, Abstraction & Planning towards trustworthy Cognitive AI Systems

1. Advanced Materials for Miniaturised Energy Harvesting Systems

Background and Scope

The exponential rise in the development and deployment of IoT (Internet of Things) systems and of connected objects (~100 billion by 2025 and ~250 billion by 2030) will in turn increase the number of sensor networks required to provide data on the ground, with estimates pointing to ~1Trillion by 2025 and ~10 trillion by 2040.

A consequence of such an expansion is a commensurate increase in energy consumption coupled with detrimental impacts on environmental sustainability: studies point to the total electricity consumed by IoTs in 2040, equalling total global energy consumption at present. Moreover, the sensor networks, generally powered by batteries, will result in 80 million batteries having to be changed each day, with knock-on effects on the wider environment.

Mitigating the impact of a rising number of such devices calls for solutions that will reduce energy consumption and increase the energy autonomy of connected sensors such as Wireless Sensor Networks (WSN) and of the IoT systems integrating such sensors.

This Challenge therefore focuses on the development of a new generation of advanced materials to deliver miniaturised integrated energy harvesting devices, with significantly enhanced performance compared to the state of the art, that will give rise to highly effective energetically autonomous devices and systems.

This Challenge supports the ambitions of the European Commission Communication "Advanced Materials for Industrial Leadership", which identified an urgent need to boost the development of advanced materials to enhance the EU's strategic autonomy in strategic fields while addressing sustainability, circularity and safety issues.

Specific Objectives

Applicants to this Challenge must address all of the following objectives:

  • The identification and development of innovative advanced materials for energy harvesting, harnessing new physical/ chemical phenomena, leading to a radical shift in application range and performance while reducing the reliance on Critical Raw Materials (CRMs)
  • The implementation of the advanced materials in a miniaturised energy harvesting module, such as, but not limited to, miniaturised solar cells, thermoelectric generators (e.g. TEG), nanotribological/ piezoelectric devices, electromagnetic wave harvesting devices
  • Integration of the miniaturised energy harvesting modules in energetically autonomous systems (e.g. wireless integrated sensors). and
  • Benchmarking the harvesting modules in a representative use case in a laboratory environment (TRL 4) with a view to demonstrating significant efficiency improvements, in terms of energy harvested, compared to the state of the art.

Leveraging digital tools such as AI to accelerate the process of identifying, designing, fabricating, and characterising these new materials is encouraged.

All proposals should also identify potential markets and the associated impacts of their innovations.

Expected Outcomes

Ambitious proposals put forward under this call will:

  • identify a new generation of advanced materials for miniaturised energy harvesting modules, and
  • achieve TRL 4 for the resulting energetically autonomous systems.

Portfolio Approach

The portfolio of selected projects should lead to the development of a variety of advanced materials applied to a range of miniaturised energy harvesting modules and final integrated systems.

To achieve this objective, the following factors will inform the choice of projects in the portfolio:

  • Phenomena exploited to harvest energy from the environment (e.g. solar, thermoelectric, piezoelectric, nanotribological etc.)
  • Composition of the proposed advanced materials, and
  • Field of application (e.g. agriculture, automotive, health monitoring, wearables, smart city management, energy management, industrial monitoring etc.).

The selected projects will be assigned to lead and/or engage in portfolio activities centred on the following priorities:

  • benchmarking to compare proposed technologies, phenomena exploited, advanced materials used and approaches
  • sharing scientific considerations, results on the different physical/chemical phenomena studied to advance the knowledge and foster new disclosures in the field with potential shifts in the paradigm
  • sharing insights on integration of the modules to solve potential issues and help advance towards delivering effective operational energetically autonomous systems
  • developing an integrated approach with different complimentary energy harvesting modules for specific use-cases and to enhance the final energy harvested, and
  • communicating to target audiences such as corporates, investors, alongside the broader public to raise awareness on the topic with a view to accelerating the adoption of these radical innovations.

Expected Impacts

The main impacts of this Challenge will be:

  • A new generation of energetically autonomous systems enabling new services that will improve the life of European citizens through applications in areas such as point of care diagnostics, smart sustainable cities etc.
  • Supporting sustainability in energy consumption and production in keeping with the ambitions of RePowerEU and the Green Deal, and
  • Enhancement of the sustainability of IoTs and energetically autonomous systems in general.

2. Biotechnology for Healthy Ageing

Background and Scope

Ageing, the gradual decline of organismal homeostasis and of physiologic functions throughout the body and mind, is a critical shared risk factor for many ageing-related chronic diseases. The EU also has an ageing society - by 2050, the share of 85+ year- olds in the EU is expected to more than double, but extended life expectancy is not matched with years spent in good health, which currently stands at 70.5 years. This will present significant social, economic and healthcare challenges and thus calls for interventions that will promote healthy longevity, as well as tools that will enable the adoption of these interventions.

Over the past decades basic research has identified hallmarks and cellular mechanisms of ageing, creating the basis for biotechnology-based or pharmaceutical therapeutic interventions, such as targeting cellular maintenance pathways, stem cell exhaustion, cellular senescence or metabolic fitness. Nonetheless, translating these insights into clinical interventions has had a low success rate, partially due to the difficulty of:

  • Translating approaches from model systems
  • Identifying when to intervene
  • Rigorous validation, and
  • End-to-end considerations of implementation (i.e. the delivery of an intervention).

This Challenge therefore looks to translate decades of ageing research into tangible biopharmaceutical solutions for healthy ageing

Specific Objectives

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 or pharmaceutical intervention that prevents, delays or reverts the onset of a specific age-related disease. Such projects must address all of the following objectives:
    • develop an intervention that targets a fundamental molecular or cellular process of ageing, such as the hallmarks of ageing
    • assess the generalisability of the intervention (showing that it is applicable more broadly to ageing-related traits beyond the primary indication targeted) by assessing the impact of the intervention on another distinct trait related to ageing
    • demonstrate proof of concept by carrying out an interventional study in a vertebrate animal model of ageing that is physiologically aged. Projects are also encouraged to include small-scale interventional clinical studies but must at a minimum anticipate how the intervention could be feasibly tested in a clinical setting, and
    • develop a plan for exploitation, which considers ethical and societal perception, economic viability and regulatory approval. At least two of these areas, considered most relevant for the intended application, must be assessed in greater depth, suitably informing the project's technology development and contributing to the portfolio activities.
  2. A biomarker based tool to enable the responsible deployment of ageing-related interventions, taking into consideration the following:
    • The tool should be based on previously identified potential biomarker candidates or ageing clocks. All types of biomarkers are welcome, for example digital, molecular or physiological biomarkers (such as frailty measurements), combinations of biomarkers and multimodal biomarkers); as well as biomarkers for different applications (e.g. predictive, diagnostics). Biomarker discovery is explicitly excluded
    • The tool should integrate different measurements of multiple molecular, anatomic, physiologic or biochemical traits, as appropriate, to comprehensively capture the ageing process (i.e. it should not exclusively measure a single parameter/hallmark of ageing)
    • The above selected biomarker signature should enable a clear linkage between clinical features and the mechanisms of ageing to be shown
    • The signature should be robust to inter-individual and intra-individual variability in ageing to provide actionable, personalised insights
    • The tool should be assessed in an initial retrospective study to establish proof- of-concept. Applicants must therefore convincingly demonstrate that they have access to suitable longevity cohorts, and
    • The selection of the biomarkers and the development of the tool should prioritise deployment feasibility and actively incorporate feedback from potential users.
  3. A New Approach Methodology (NAM) that goes beyond the current state-of- the art to enable the future development of interventions for healthy ageing. The NAM should:
    • robustly capture the aged status of the system and the systemic/integrative nature of ageing (i.e. it should capture more than 1 molecular or cellular aspect of ageing, and more than 1 tissue type)
    • be benchmarked against a relevant animal model of ageing, and
    • be tested in the setting of a clearly specified use case (e.g. development of an intervention; as pre-clinical model in a regulatory setting).

Precision nutrition, the development of novel ageing clocks and wellness applications fall outside the scope of this Challenge.

All proposals should consider biological sex and gender-specific health determinants in their development, with reproductive ageing also in scope.

Expected Outcomes

Ambitious proposals put forward under this call will deliver:

  • 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.

Portfolio approach

The portfolio of projects selected under this Challenge will ensure a coverage of projects developing interventions, biomarkers and NAMs, considering the following guidelines for portfolio composition:

  • Interventions: no more than 5 projects. The portfolio of selected projects should collectively address a variety molecular and cellular processes related to ageing, and a variety of different age-related diseases.
  • Biomarkers: no more than 3 projects. Projects will be selected to capture different application areas (diagnostic, predictive, prognostic biomarker), with preference to the inclusion of at least one diagnostic biomarker).
  • NAMs: No more than 2 projects. Projects will be selected to capture a diversity of approaches to assess aged status in NAMs, different tissues or cell types and different use cases.

The selected consortia will benefit from mutual learning, and the exchange of expertise. Consortia will be encouraged to collaborate to address the following shared challenges faced by all ageing interventions:

  • Scientific: preclinical models; Biomarkers of healthy/unhealthy ageing
  • Path to Market: Defining suitable regulatory pathways, and
  • Societal: Acceptance and role of preventative / therapeutic interventions, Need for improved longevity literacy.

Expected Impacts

This Challenge looks to accelerate the development and uptake of clinically validated interventions that target the root cause of multiple age-related morbidities. It will:

  • Deliver biotechnology-based interventions for healthy ageing
  • Accelerate the implementation of personalised care in ageing based on molecular phenotyping
  • Provide recommendations for regulatory pathways addressing ageing as a target to inform developers, regulators, and other decision makers, and
  • Improve citizen literacy on longevity.

3. DeepRAP: Deep Reasoning, Abstraction & Planning towards trustworthy Cognitive AI Systems

Background and Scope

Artificial Intelligence (AI) systems have achieved remarkable progress as evidenced by the ability of Generative AI to recognise patterns and generate contextually relevant outputs based on ever larger models and associated datasets. However, despite the remarkable strides made over the past decade, there remains a significant gap between the capabilities of the human brain and machine intelligence, which must be overcome to achieve robust performance and enable effective interactions with users and stakeholders.

Current Generative AI models can release very accurate outputs and even solve some mathematical problems but might struggle with some complex reasoning benchmarks and to understand the real world. These models frequently fail to reliably solve logic tasks and long-term planning, even when provably correct solutions exist, limiting their effectiveness in critical applications where precision is essential.

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.

This will overcome the limitations of current deep learning models, which despite their strengths, have limitations in critical cognitive functions for abstraction, contextualisation, causality, explainability, and intelligible reasoning — competencies that are fundamental to move towards human-like intelligence.

Specific Objectives

Innovative ideas put forward under this Challenge must explore novel approaches, including combinations of existing techniques (i.e. neuro-symbolic AI), or the creation of entirely new frameworks that go beyond current, traditional, deep learning and reinforcement learning paradigms. These could be inspired by developments in diverse fields such as neuroscience, biology, physics, philosophy and more.

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. This requires shifting from purely data- driven correlations to AI systems capable of understanding why patterns emerge, identifying underlying causes, and drawing valid conclusions through both deductive and inductive processes. Neuro-symbolic approaches, which combine the learning power of neural networks with the structured inference of symbolic reasoning are particularly encouraged to advance these capabilities. Integrating contextual and commonsense knowledge enables AI to interpret information more holistically, adapt decisions dynamically, and handle ambiguity and uncertainty. Deep reasoning systems should be able to reconcile multiple sources of information, provide transparent and explainable rationales for their outputs, and align with human values and expectations, ensuring trustworthy and accountable operations in demanding real-world scenarios.
  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. This includes the development of internal world models to support abstraction, foster commonsense understanding, and integrate semantic and contextual awareness. Approaches that combine symbolic reasoning, analogical mapping, and representation learning are particularly encouraged, as they empower AI to interpret meaning, intent, and relationships within complex environments. Progress in deep abstraction is essential for achieving cognitive flexibility, robust transfer learning, and adaptive reasoning in dynamic, data-scarce, or rapidly evolving settings.
  3. Deep Planning: Developing robust, adaptive, and scalable planning algorithms/models capable of operating in open-world, agentic, or uncertain real- time environments. This involves leveraging advanced deep learning techniques such as deep reinforcement learning and architectures tailored for planning tasks to enable AI systems to autonomously devise, optimise, and adjust complex strategies in dynamic settings. Neuro-symbolic approaches integrating neural networks with symbolic reasoning are particularly encouraged to address uncertainty, provide formal guarantees, and enable explainable, dependable decision-making. Emphasis is placed on long-term, flexible planning that incorporates cognitive timing and predictive modelling, enabling systems to anticipate and adapt within dynamic contexts. Approaches should explore hierarchical planning across multiple temporal levels, contingency planning for effective fallback strategies, and continual re- planning to dynamically update plans as environments evolve. These advancements will underpin resilient, coordinated, and trustworthy AI planning in complex, unpredictable scenarios.

Expected Outcomes

Ambitious proposals put forward under this call will deliver:

  • Models and/or architectures that handle multimodal data and knowledge, uncertainty, and can be trained and deployed with constrained computational resources
  • Provable trustworthiness mechanisms ensuring explainability, transparency, fairness, risk evaluation, security and alignment with ethical and legal standards, including the EU AI Act, and
  • Demonstrate the developed capabilities integrated in a cognitive AI system (reaching TRL4) performing complex real-world tasks (e.g., scientific discovery, decision support, problem solving) as well as simulations at a scale.

In addition, proposals will:

  • Propose new methods and metrics for evaluating and certifying reasoning and trustworthiness in AI as well as the use of the computational resources
  • Follow the FAIR principles ensuring all data, models, and results are Findable, Accessible, Interoperable, and Reusable to maximise transparency, reproducibility, and impact, and
  • Develop synergies with EU initiatives such as TEFs (AI Testing and Experimentation Facilities), eBrains, Resource for AI Science in Europe (RAISE), AI-on-demand Platform (AIoD) and the Quantum Flagship.

Portfolio approach

The composition of the portfolio of projects to be funded under the DeepRAP Challenge will ensure comprehensive coverage across the following categories with a view to ensuring breadth and enabling synergies between the projects:

  • Category 1 – Cognitive Function Capability: Reasoning, abstraction, and planning should be covered by the selected portfolio.
  • Category 2 – Technological Approach: The selected projects are expected to use a variety of technological approaches, including but not limited to, neuro-symbolic AI, deep learning, reinforcement learning, and novel frameworks inspired by interdisciplinary fields, and
  • Category 3 – Use Case and Application Domain: The selected projects will cover a variety of real-world domains, such as industry, mobility, civil security, scientific discovery, health, cybersecurity, and human-robot interaction.

The selected projects will also be assigned to lead and/or engage in portfolio activities centred on the following priorities:

  • Interoperability: Establishing common standards and protocols to ensure seamless alignment between projects
  • Benchmark Development: Co-creating a DeepRAP benchmark with shared tasks and an open evaluation platform for transparent assessment
  • Common Pilots: Delivering joint pilot demonstrations addressing complex real-world problems to showcase DeepRAP capabilities
  • Multiagent Integration: where feasible, combining project outcomes into modular, multiagent AI systems demonstrating collective reasoning and planning through structured interactions among multiple agents
  • Application Shaping: Defining impactful use cases and engaging stakeholders to guide the development and adoption of innovative cognitive AI systems, and
  • Ethical and Societal Alignment: Proactively addressing ethical, legal, and societal considerations, including transparency, privacy, safety, and fairness of cognitive AI systems.

Expected Impact

The resulting portfolio will not only advance the scientific state-of-the-art but also build a robust, interoperable, and application-driven community, positioning Europe at the forefront of trustworthy cognitive AI. It should also lay the foundations for future European leadership in safe, human-centric cognitive AI, supporting sovereignty and competitiveness in key sectors. It will support the ambitions of the AI Act and the European approach to Artificial Intelligence.