CROWDHELIX LIMITED

The aim of the current project is to bring about the development of the full demonstrator of DiCiM, a set of integrated digital solutions that makes use of Internet of Things (IoT), Machine Learning (ML) based Artificial Intelligence (AI), Big Data, Image Processing and Augmented Reality (AR) to support different actors of the industrial value chain such as managers, engineers and operators in their decision making and to carry out value recovery activities for circular economy. The integrated digital solutions include an open access digital platform for lifecycle information management and support solutions for value recovery activities. DiCiM project revolves around the value use and value recovery phases of the Circular Value Model (CVM) with the specific data, technology and the management needed to support its implementation. In particular, it focuses on integrated digital solutions to enabling condition monitoring during the use phase, optimizing the reverse logistics, and achieving efficiency and responsiveness in the value recovery activities (i.e. collection, inspection, sorting, disassembly, testing and repairing/refurbishing/remanufacturing/recycling) to enable reuse of products, parts and materials. DiCiM project will demonstrate integrated digital solutions in four use cases in three industrial sectors that represent over two thirds of the European Economy. They include whitegoods (i.e. refrigerators, washing machines), electronics (i.e. printers) and automotive. With important gains such as increase in spare part recovery efficiency by 90% (which means 126 000 labour hours are saved for refrigerator demonstrator) and increase in spare part recovery rate by 20% (about 2920 tons net material saving for washing machines and 6000 tons for automotive). DiCiM digital solutions will boost new circular economy business models based on value recovery activities to sustain and encourage remanufacturing throughout Europe.

EC contribution:

EUR 370k

Start:

2023-01-01

End:

2026-12-31

Status:

SIGNED

Scheme:

HORIZON-RIA

Call:

HORIZON-CL4-2022-TWIN-TRANSITION-01

View on CORDIS (DOI 10.3030/101091536)

Artificial Intelligence (AI) is increasingly used in the employment sector to manage and control individual workers. One type of AI is Natural Language Processing (NLP) based tools that can analyze text to make inferences or decisions. A recent Sage study found that 24% of companies used AI for hiring purposes. In an employment context, this can involve analyzing text created by an employee or recruitment candidate in order to assist management in deciding to invite a candidate for an interview, to training and employee engagement, or to monitor for infractions that could lead to disciplinary proceedings. However, the models that NLP-based systems are based on are biased. Additionally, it has been shown that bias in an underlying AI model is reproduced in applications based on that model). This can lead to biased decisions that run contrary to the goals of the European Pillar of Social Rights in relationship to work and employment, specifically Pillar 2 (Gender Equality), Pillar 3 (Equal Opportunity), Pillar 5 (Secure and Adaptable Employment) and the United Nations’ (UN) Sustainable Development Goals (SDGs), specifically SDG 5 (Gender Equality), SDG 8 (Decent Work and Economic Growth). It is therefore necessary to identify and mitigate biases that occur in applications used in a Human Resources Management (HRM) context. Addressing such concerns in an employment context is especially relevant, as most existing European studies on employment discrimination have indeed found that discrimination exists, both when considering individual diversity criteria and multiple criteria in intersectional analyses. In order to investigate and mitigate these biases, we apply this “BIAS”-project, for mitigating diversity biases of AI in the labor market. The chief technical objective of BIAS is the development of a proof-of-concept for an innovative technology based on Natural Language Processing (NLP) and Case Based Reasoning (CBR) for use in an HR recruitment use case.

EC contribution:

EUR 325k

Start:

2022-11-01

End:

2026-10-31

Status:

SIGNED

Scheme:

HORIZON-RIA

Call:

HORIZON-CL4-2021-HUMAN-01

View on CORDIS (DOI 10.3030/101070468)

Robots hold great potential to support humans in extreme environments—such as post-disaster zones—where conditions are hazardous, unpredictable, and often lack reliable connectivity. Their capacity to operate in dangerous or inaccessible areas makes them crucial for search and rescue, damage assessment, and emergency response. Yet, conventional robotic systems often struggle in such contexts due to limited adaptability, fragile autonomy, and insufficient coordination. Challenges like degraded sensor inputs, dynamic unstructured terrains, GPS-denied environments, and constrained communications further hinder their effectiveness. While embodied AI has significantly enhanced the perception and action capabilities of individual robots, its focus has largely remained on single-agent systems. Achieving efficient multi-robot collaboration under uncertain, real-time, and bandwidth-limited conditions remains a major research frontier. The DIRECT project tackles these challenges by developing an innovative distributed machine learning framework that enables a fleet of robots to collaboratively perceive, reason, plan, and act in extreme environments.

EC contribution:

EUR 85k

Start:

2026-09-01

End:

2030-08-31

Status:

SIGNED

Scheme:

HORIZON-TMA-MSCA-SE

Call:

HORIZON-MSCA-2025-SE-01

View on CORDIS (DOI 10.3030/101299316)