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Request date Organisation name Country Search type Topic Link
05/04/2023 Sima Sinaei Vatican City Expertise Request Piloting emerging Smart IoT Platforms and decentralized intelligence (IA) (HORIZON-CL4-2024-DATA-01-03) F&T portal
Distributed Artificial Intelligence Systems -
Federated learning: Federated Learning (FL) is a distributed machine learning approach that enables devices in the computing continuum to collaboratively learn a shared model while keeping data locally. FL can be used to optimise the performance of AI models by leveraging data from across the continuum, while still ensuring data privacy by not sharing raw data. Techniques that shall be explored include stochastic gradient descent.
05/04/2023 Sima Sinaei Vatican City Expertise Request Piloting emerging Smart IoT Platforms and decentralized intelligence (IA) (HORIZON-CL4-2024-DATA-01-03) F&T portal
Distributed Artificial Intelligence Systems -
Federated learning: Federated Learning (FL) is a distributed machine learning approach that enables devices in the computing continuum to collaboratively learn a shared model while keeping data locally. FL can be used to optimise the performance of AI models by leveraging data from across the continuum, while still ensuring data privacy by not sharing raw data. Techniques that shall be explored include stochastic gradient descent.
05/04/2023 Sima Sinaei Vatican City Expertise Request Piloting emerging Smart IoT Platforms and decentralized intelligence (IA) (HORIZON-CL4-2024-DATA-01-03) F&T portal
Distributed Artificial Intelligence Systems -
Federated learning: Federated Learning (FL) is a distributed machine learning approach that enables devices in the computing continuum to collaboratively learn a shared model while keeping data locally. FL can be used to optimise the performance of AI models by leveraging data from across the continuum, while still ensuring data privacy by not sharing raw data. Techniques that shall be explored include stochastic gradient descent.
05/04/2023 Sima Sinaei Vatican City Expertise Request Piloting emerging Smart IoT Platforms and decentralized intelligence (IA) (HORIZON-CL4-2024-DATA-01-03) F&T portal
Distributed Artificial Intelligence Systems -
Federated learning: Federated Learning (FL) is a distributed machine learning approach that enables devices in the computing continuum to collaboratively learn a shared model while keeping data locally. FL can be used to optimise the performance of AI models by leveraging data from across the continuum, while still ensuring data privacy by not sharing raw data. Techniques that shall be explored include stochastic gradient descent.
05/04/2023 Sima Sinaei Vatican City Expertise Request Piloting emerging Smart IoT Platforms and decentralized intelligence (IA) (HORIZON-CL4-2024-DATA-01-03) F&T portal
Distributed Artificial Intelligence Systems -
Federated learning: Federated Learning (FL) is a distributed machine learning approach that enables devices in the computing continuum to collaboratively learn a shared model while keeping data locally. FL can be used to optimise the performance of AI models by leveraging data from across the continuum, while still ensuring data privacy by not sharing raw data. Techniques that shall be explored include stochastic gradient descent.
05/04/2023 Sima Sinaei Vatican City Expertise Request Piloting emerging Smart IoT Platforms and decentralized intelligence (IA) (HORIZON-CL4-2024-DATA-01-03) F&T portal
Distributed Artificial Intelligence Systems -
Federated learning: Federated Learning (FL) is a distributed machine learning approach that enables devices in the computing continuum to collaboratively learn a shared model while keeping data locally. FL can be used to optimise the performance of AI models by leveraging data from across the continuum, while still ensuring data privacy by not sharing raw data. Techniques that shall be explored include stochastic gradient descent.
05/04/2023 Sima Sinaei Vatican City Expertise Request Piloting emerging Smart IoT Platforms and decentralized intelligence (IA) (HORIZON-CL4-2024-DATA-01-03) F&T portal
Distributed Artificial Intelligence Systems -
Federated learning: Federated Learning (FL) is a distributed machine learning approach that enables devices in the computing continuum to collaboratively learn a shared model while keeping data locally. FL can be used to optimise the performance of AI models by leveraging data from across the continuum, while still ensuring data privacy by not sharing raw data. Techniques that shall be explored include stochastic gradient descent.
05/04/2023 Sima Sinaei Vatican City Expertise Request Piloting emerging Smart IoT Platforms and decentralized intelligence (IA) (HORIZON-CL4-2024-DATA-01-03) F&T portal
Distributed Artificial Intelligence Systems -
Federated learning: Federated Learning (FL) is a distributed machine learning approach that enables devices in the computing continuum to collaboratively learn a shared model while keeping data locally. FL can be used to optimise the performance of AI models by leveraging data from across the continuum, while still ensuring data privacy by not sharing raw data. Techniques that shall be explored include stochastic gradient descent.
05/04/2023 Sima Sinaei Vatican City Expertise Request Piloting emerging Smart IoT Platforms and decentralized intelligence (IA) (HORIZON-CL4-2024-DATA-01-03) F&T portal
Distributed Artificial Intelligence Systems -
Federated learning: Federated Learning (FL) is a distributed machine learning approach that enables devices in the computing continuum to collaboratively learn a shared model while keeping data locally. FL can be used to optimise the performance of AI models by leveraging data from across the continuum, while still ensuring data privacy by not sharing raw data. Techniques that shall be explored include stochastic gradient descent.
05/04/2023 Sima Sinaei Vatican City Expertise Request Piloting emerging Smart IoT Platforms and decentralized intelligence (IA) (HORIZON-CL4-2024-DATA-01-03) F&T portal
Distributed Artificial Intelligence Systems -
Federated learning: Federated Learning (FL) is a distributed machine learning approach that enables devices in the computing continuum to collaboratively learn a shared model while keeping data locally. FL can be used to optimise the performance of AI models by leveraging data from across the continuum, while still ensuring data privacy by not sharing raw data. Techniques that shall be explored include stochastic gradient descent.
05/04/2023 Tisalabs Ireland Expertise Request Reliable Services and Smart Security (HORIZON-JU-SNS-2023-STREAM-B-01-04) F&T portal
We are looking to participate into this call and we would like to get with organizations that have a use case.
We are a security company focusing on edge software deployment and management as well as running AI/ML at the dge.
05/04/2023 Sima Sinaei Vatican City Expertise Request Piloting emerging Smart IoT Platforms and decentralized intelligence (IA) (HORIZON-CL4-2024-DATA-01-03) F&T portal
Distributed Artificial Intelligence Systems -
Federated learning: Federated Learning (FL) is a distributed machine learning approach that enables devices in the computing continuum to collaboratively learn a shared model while keeping data locally. FL can be used to optimise the performance of AI models by leveraging data from across the continuum, while still ensuring data privacy by not sharing raw data. Techniques that shall be explored include stochastic gradient descent.
05/04/2023 Sima Sinaei Vatican City Expertise Request Piloting emerging Smart IoT Platforms and decentralized intelligence (IA) (HORIZON-CL4-2024-DATA-01-03) F&T portal
Distributed Artificial Intelligence Systems -
Federated learning: Federated Learning (FL) is a distributed machine learning approach that enables devices in the computing continuum to collaboratively learn a shared model while keeping data locally. FL can be used to optimise the performance of AI models by leveraging data from across the continuum, while still ensuring data privacy by not sharing raw data. Techniques that shall be explored include stochastic gradient descent.
05/04/2023 Sima Sinaei Vatican City Expertise Request Piloting emerging Smart IoT Platforms and decentralized intelligence (IA) (HORIZON-CL4-2024-DATA-01-03) F&T portal
Distributed Artificial Intelligence Systems -
Federated learning: Federated Learning (FL) is a distributed machine learning approach that enables devices in the computing continuum to collaboratively learn a shared model while keeping data locally. FL can be used to optimise the performance of AI models by leveraging data from across the continuum, while still ensuring data privacy by not sharing raw data. Techniques that shall be explored include stochastic gradient descent.
05/04/2023 Sima Sinaei Vatican City Expertise Request Piloting emerging Smart IoT Platforms and decentralized intelligence (IA) (HORIZON-CL4-2024-DATA-01-03) F&T portal
Distributed Artificial Intelligence Systems -
Federated learning: Federated Learning (FL) is a distributed machine learning approach that enables devices in the computing continuum to collaboratively learn a shared model while keeping data locally. FL can be used to optimise the performance of AI models by leveraging data from across the continuum, while still ensuring data privacy by not sharing raw data. Techniques that shall be explored include stochastic gradient descent.
05/04/2023 Sima Sinaei Vatican City Expertise Request Piloting emerging Smart IoT Platforms and decentralized intelligence (IA) (HORIZON-CL4-2024-DATA-01-03) F&T portal
Distributed Artificial Intelligence Systems -
Federated learning: Federated Learning (FL) is a distributed machine learning approach that enables devices in the computing continuum to collaboratively learn a shared model while keeping data locally. FL can be used to optimise the performance of AI models by leveraging data from across the continuum, while still ensuring data privacy by not sharing raw data. Techniques that shall be explored include stochastic gradient descent.
05/04/2023 Sima Sinaei Vatican City Expertise Request Piloting emerging Smart IoT Platforms and decentralized intelligence (IA) (HORIZON-CL4-2024-DATA-01-03) F&T portal
Distributed Artificial Intelligence Systems -
Federated learning: Federated Learning (FL) is a distributed machine learning approach that enables devices in the computing continuum to collaboratively learn a shared model while keeping data locally. FL can be used to optimise the performance of AI models by leveraging data from across the continuum, while still ensuring data privacy by not sharing raw data. Techniques that shall be explored include stochastic gradient descent.
05/04/2023 Sima Sinaei Vatican City Expertise Request Piloting emerging Smart IoT Platforms and decentralized intelligence (IA) (HORIZON-CL4-2024-DATA-01-03) F&T portal
Distributed Artificial Intelligence Systems -
Federated learning: Federated Learning (FL) is a distributed machine learning approach that enables devices in the computing continuum to collaboratively learn a shared model while keeping data locally. FL can be used to optimise the performance of AI models by leveraging data from across the continuum, while still ensuring data privacy by not sharing raw data. Techniques that shall be explored include stochastic gradient descent.
05/04/2023 Sima Sinaei Vatican City Expertise Request Piloting emerging Smart IoT Platforms and decentralized intelligence (IA) (HORIZON-CL4-2024-DATA-01-03) F&T portal
Distributed Artificial Intelligence Systems -
Federated learning: Federated Learning (FL) is a distributed machine learning approach that enables devices in the computing continuum to collaboratively learn a shared model while keeping data locally. FL can be used to optimise the performance of AI models by leveraging data from across the continuum, while still ensuring data privacy by not sharing raw data. Techniques that shall be explored include stochastic gradient descent.
05/04/2023 Sima Sinaei Vatican City Expertise Request Piloting emerging Smart IoT Platforms and decentralized intelligence (IA) (HORIZON-CL4-2024-DATA-01-03) F&T portal
Distributed Artificial Intelligence Systems -
Federated learning: Federated Learning (FL) is a distributed machine learning approach that enables devices in the computing continuum to collaboratively learn a shared model while keeping data locally. FL can be used to optimise the performance of AI models by leveraging data from across the continuum, while still ensuring data privacy by not sharing raw data. Techniques that shall be explored include stochastic gradient descent.
05/04/2023 Sima Sinaei Vatican City Expertise Request Piloting emerging Smart IoT Platforms and decentralized intelligence (IA) (HORIZON-CL4-2024-DATA-01-03) F&T portal
Distributed Artificial Intelligence Systems -
Federated learning: Federated Learning (FL) is a distributed machine learning approach that enables devices in the computing continuum to collaboratively learn a shared model while keeping data locally. FL can be used to optimise the performance of AI models by leveraging data from across the continuum, while still ensuring data privacy by not sharing raw data. Techniques that shall be explored include stochastic gradient descent.
05/04/2023 Sima Sinaei Vatican City Expertise Request Piloting emerging Smart IoT Platforms and decentralized intelligence (IA) (HORIZON-CL4-2024-DATA-01-03) F&T portal
Distributed Artificial Intelligence Systems -
Federated learning: Federated Learning (FL) is a distributed machine learning approach that enables devices in the computing continuum to collaboratively learn a shared model while keeping data locally. FL can be used to optimise the performance of AI models by leveraging data from across the continuum, while still ensuring data privacy by not sharing raw data. Techniques that shall be explored include stochastic gradient descent.
05/04/2023 Sima Sinaei Vatican City Expertise Request Piloting emerging Smart IoT Platforms and decentralized intelligence (IA) (HORIZON-CL4-2024-DATA-01-03) F&T portal
Distributed Artificial Intelligence Systems -
Federated learning: Federated Learning (FL) is a distributed machine learning approach that enables devices in the computing continuum to collaboratively learn a shared model while keeping data locally. FL can be used to optimise the performance of AI models by leveraging data from across the continuum, while still ensuring data privacy by not sharing raw data. Techniques that shall be explored include stochastic gradient descent.
05/04/2023 Sima Sinaei Vatican City Expertise Request Piloting emerging Smart IoT Platforms and decentralized intelligence (IA) (HORIZON-CL4-2024-DATA-01-03) F&T portal
Distributed Artificial Intelligence Systems -
Federated learning: Federated Learning (FL) is a distributed machine learning approach that enables devices in the computing continuum to collaboratively learn a shared model while keeping data locally. FL can be used to optimise the performance of AI models by leveraging data from across the continuum, while still ensuring data privacy by not sharing raw data. Techniques that shall be explored include stochastic gradient descent.
05/04/2023 Sima Sinaei Vatican City Expertise Request Piloting emerging Smart IoT Platforms and decentralized intelligence (IA) (HORIZON-CL4-2024-DATA-01-03) F&T portal
Distributed Artificial Intelligence Systems -
Federated learning: Federated Learning (FL) is a distributed machine learning approach that enables devices in the computing continuum to collaboratively learn a shared model while keeping data locally. FL can be used to optimise the performance of AI models by leveraging data from across the continuum, while still ensuring data privacy by not sharing raw data. Techniques that shall be explored include stochastic gradient descent.
05/04/2023 Sima Sinaei Vatican City Expertise Request Piloting emerging Smart IoT Platforms and decentralized intelligence (IA) (HORIZON-CL4-2024-DATA-01-03) F&T portal
Distributed Artificial Intelligence Systems -
Federated learning: Federated Learning (FL) is a distributed machine learning approach that enables devices in the computing continuum to collaboratively learn a shared model while keeping data locally. FL can be used to optimise the performance of AI models by leveraging data from across the continuum, while still ensuring data privacy by not sharing raw data. Techniques that shall be explored include stochastic gradient descent.
05/04/2023 Sima Sinaei Vatican City Expertise Request Piloting emerging Smart IoT Platforms and decentralized intelligence (IA) (HORIZON-CL4-2024-DATA-01-03) F&T portal
Distributed Artificial Intelligence Systems -
Federated learning: Federated Learning (FL) is a distributed machine learning approach that enables devices in the computing continuum to collaboratively learn a shared model while keeping data locally. FL can be used to optimise the performance of AI models by leveraging data from across the continuum, while still ensuring data privacy by not sharing raw data. Techniques that shall be explored include stochastic gradient descent.
05/04/2023 Sima Sinaei Vatican City Expertise Request Piloting emerging Smart IoT Platforms and decentralized intelligence (IA) (HORIZON-CL4-2024-DATA-01-03) F&T portal
Distributed Artificial Intelligence Systems -
Federated learning: Federated Learning (FL) is a distributed machine learning approach that enables devices in the computing continuum to collaboratively learn a shared model while keeping data locally. FL can be used to optimise the performance of AI models by leveraging data from across the continuum, while still ensuring data privacy by not sharing raw data. Techniques that shall be explored include stochastic gradient descent.
05/04/2023 Sima Sinaei Vatican City Expertise Request Piloting emerging Smart IoT Platforms and decentralized intelligence (IA) (HORIZON-CL4-2024-DATA-01-03) F&T portal
Distributed Artificial Intelligence Systems -
Federated learning: Federated Learning (FL) is a distributed machine learning approach that enables devices in the computing continuum to collaboratively learn a shared model while keeping data locally. FL can be used to optimise the performance of AI models by leveraging data from across the continuum, while still ensuring data privacy by not sharing raw data. Techniques that shall be explored include stochastic gradient descent.
05/04/2023 Sima Sinaei Vatican City Expertise Request Piloting emerging Smart IoT Platforms and decentralized intelligence (IA) (HORIZON-CL4-2024-DATA-01-03) F&T portal
Distributed Artificial Intelligence Systems -
Federated learning: Federated Learning (FL) is a distributed machine learning approach that enables devices in the computing continuum to collaboratively learn a shared model while keeping data locally. FL can be used to optimise the performance of AI models by leveraging data from across the continuum, while still ensuring data privacy by not sharing raw data. Techniques that shall be explored include stochastic gradient descent.
05/04/2023 Sima Sinaei Vatican City Expertise Request Piloting emerging Smart IoT Platforms and decentralized intelligence (IA) (HORIZON-CL4-2024-DATA-01-03) F&T portal
Distributed Artificial Intelligence Systems -
Federated learning: Federated Learning (FL) is a distributed machine learning approach that enables devices in the computing continuum to collaboratively learn a shared model while keeping data locally. FL can be used to optimise the performance of AI models by leveraging data from across the continuum, while still ensuring data privacy by not sharing raw data. Techniques that shall be explored include stochastic gradient descent.
05/04/2023 Sima Sinaei Vatican City Expertise Request Piloting emerging Smart IoT Platforms and decentralized intelligence (IA) (HORIZON-CL4-2024-DATA-01-03) F&T portal
Distributed Artificial Intelligence Systems -
Federated learning: Federated Learning (FL) is a distributed machine learning approach that enables devices in the computing continuum to collaboratively learn a shared model while keeping data locally. FL can be used to optimise the performance of AI models by leveraging data from across the continuum, while still ensuring data privacy by not sharing raw data. Techniques that shall be explored include stochastic gradient descent.
05/04/2023 Sima Sinaei Vatican City Expertise Request Piloting emerging Smart IoT Platforms and decentralized intelligence (IA) (HORIZON-CL4-2024-DATA-01-03) F&T portal
Distributed Artificial Intelligence Systems -
Federated learning: Federated Learning (FL) is a distributed machine learning approach that enables devices in the computing continuum to collaboratively learn a shared model while keeping data locally. FL can be used to optimise the performance of AI models by leveraging data from across the continuum, while still ensuring data privacy by not sharing raw data. Techniques that shall be explored include stochastic gradient descent.
05/04/2023 Sima Sinaei Vatican City Expertise Request Piloting emerging Smart IoT Platforms and decentralized intelligence (IA) (HORIZON-CL4-2024-DATA-01-03) F&T portal
Distributed Artificial Intelligence Systems -
Federated learning: Federated Learning (FL) is a distributed machine learning approach that enables devices in the computing continuum to collaboratively learn a shared model while keeping data locally. FL can be used to optimise the performance of AI models by leveraging data from across the continuum, while still ensuring data privacy by not sharing raw data. Techniques that shall be explored include stochastic gradient descent.
04/04/2023 Consortium Ubiquitous Technologies S.c.a.r.l. Italy Expertise Request System Architecture (HORIZON-JU-SNS-2023-STREAM-B-01-01) F&T portal
Cubit is a SME specialised in the design and realisation of custom electronic devices, monitoring and control systems, and IoT solutions for industrial automation, smart city, wearables, etc. We cover from hardware design to software development, including complex and intelligent algorithms on resource-constrained devices, interoperability and integration with larger systems. We have experience with numerous wired / wireless communication technologies, including design of proprietary protocols.
04/04/2023 NUROMEDIA GMBH Germany Expertise Request SNS Societal Challenges (HORIZON-JU-SNS-2023-STREAM-CSA-01) F&T portal
Nuromedia GmbH is a German software engineering & multimedia company with more than 15 years of experience in national and EU funded projects. Our team offers competences like software engineering, gamification, 2D/3D animation, UI/UX design, AR, MR & VR development, smart city, 5G, IoT, big data,digital twin and machine learning/AI. Our industry focus is Health, Energy, Telecommunication, E-learning, Education, Industry 4.0, Agriculture, Automotive. Contact info: [email protected]
04/04/2023 Consortium Ubiquitous Technologies S.c.a.r.l. Italy Expertise Request Communication Infrastructure Technologies and Devices (HORIZON-JU-SNS-2023-STREAM-B-01-03) F&T portal
Cubit is a SME specialised in IoT solutions and drones. We design and realise IoT systems for multiple markets, covering from hardware design to software development, including intelligent algorithms on resource-constrained devices, interoperability, integration with larger systems. We have experience with many wired/wireless communication technologies, and design of proprietary protocols. We realise unmanned vehicles of various sizes and for multiple applications, including custom auto-pilots.
04/04/2023 NUROMEDIA GMBH Germany Expertise Request Microelectronics-based Solutions for 6G Networks (HORIZON-JU-SNS-2023-STREAM-B-01-05) F&T portal
Nuromedia GmbH is a German software engineering & multimedia company with more than 15 years of experience in national and EU funded projects. Our team offers competences like software engineering, gamification, 2D/3D animation, UI/UX design, AR, MR & VR development, smart city, 5G, IoT, big data,digital twin and machine learning/AI. Our industry focus is Health, Energy, Telecommunication, E-learning, Education, Industry 4.0, Agriculture, Automotive. Contact info: [email protected]
04/04/2023 NUROMEDIA GMBH Germany Expertise Request SNS Large Scale Trials and Pilots (LST&Ps) with Verticals – Focused Topic (HORIZON-JU-SNS-2023-STREAM-D-01-01) F&T portal
Nuromedia GmbH is a German software engineering & multimedia company with more than 15 years of experience in national and EU funded projects. Our team offers competences like software engineering, gamification, 2D/3D animation, UI/UX design, AR, MR & VR development, smart city, 5G, IoT, big data,digital twin and machine learning/AI. Our industry focus is Health, Energy, Telecommunication, E-learning, Education, Industry 4.0, Agriculture, Automotive. Contact info: [email protected]
04/04/2023 NUROMEDIA GMBH Germany Expertise Request Wireless Communication Technologies and Signal Processing (HORIZON-JU-SNS-2023-STREAM-B-01-02) F&T portal
Nuromedia GmbH is a German software engineering & multimedia company with more than 15 years of experience in national and EU funded projects. Our team offers competences like software engineering, gamification, 2D/3D animation, UI/UX design, AR, MR & VR development, smart city, 5G, IoT, big data,digital twin and machine learning/AI. Our industry focus is Health, Energy, Telecommunication, E-learning, Education, Industry 4.0, Agriculture, Automotive. Contact info: [email protected]
04/04/2023 UNIVERSITATSMEDIZIN ROSTOCK Germany Expertise Request Pilot line(s) for 2D materials-based devices (RIA) (HORIZON-CL4-2024-DIGITAL-EMERGING-01-31) F&T portal
The Institute for Biomedical Engineering (IBMT) at Rostock University Medical Center performs research in the area of biomaterials and implant technology. We design and prototype novel implantable medical devices, implant-based coatings for local drug delivery, as well as functionalized biomaterials. We run a GLP-certified lab for preclinical (in vitro, in vivo) biocompatibility testing and combination product analytics (drug release, stability, degradation).More info: ibmt.med.uni-rostock.de/en
04/04/2023 NUROMEDIA GMBH Germany Expertise Request Complementary SNS experimental Pan-EU federated Infrastructure (HORIZON-JU-SNS-2023-STREAM-C-01-01) F&T portal
Nuromedia GmbH is a German software engineering & multimedia company with more than 15 years of experience in national and EU funded projects. Our team offers competences like software engineering, gamification, 2D/3D animation, UI/UX design, AR, MR & VR development, smart city, 5G, IoT, big data,digital twin and machine learning/AI. Our industry focus is Health, Energy, Telecommunication, E-learning, Education, Industry 4.0, Agriculture, Automotive. Contact info: [email protected]
04/04/2023 INFOCERT SPA Italy Expertise Request Reliable Services and Smart Security (HORIZON-JU-SNS-2023-STREAM-B-01-04) F&T portal
InfoCert is the largest Trust Service Provider in EU, providing trust and identity services (both according the traditional and the decentralised paradigm) which apply to natural/legal persons as well as to infrastructural components. We are willing to bring our expertise on trust frameworks and technologies to a consortium which could benefit from our contribution
04/04/2023 NUROMEDIA GMBH Germany Expertise Request System Architecture (HORIZON-JU-SNS-2023-STREAM-B-01-01) F&T portal
Nuromedia GmbH is a German software engineering & multimedia company with more than 15 years of experience in national and EU funded projects. Our team offers competences like software engineering, gamification, 2D/3D animation, UI/UX design, AR, MR & VR development, smart city, 5G, IoT, big data,digital twin and machine learning/AI. Our industry focus is Health, Energy, Telecommunication, E-learning, Education, Industry 4.0, Agriculture, Automotive. Contact info: [email protected]
04/04/2023 NUROMEDIA GMBH Germany Expertise Request EU-US 6G R&I Cooperation (HORIZON-JU-SNS-2023-STREAM-B-01-06) F&T portal
Nuromedia GmbH is a German software engineering & multimedia company with more than 15 years of experience in national and EU funded projects. Our team offers competences like software engineering, gamification, 2D/3D animation, UI/UX design, AR, MR & VR development, smart city, 5G, IoT, big data,digital twin and machine learning/AI. Our industry focus is Health, Energy, Telecommunication, E-learning, Education, Industry 4.0, Agriculture, Automotive. Contact info: [email protected]
04/04/2023 NUROMEDIA GMBH Germany Expertise Request Communication Infrastructure Technologies and Devices (HORIZON-JU-SNS-2023-STREAM-B-01-03) F&T portal
Nuromedia GmbH is a German software engineering & multimedia company with more than 15 years of experience in national and EU funded projects. Our team offers competences like software engineering, gamification, 2D/3D animation, UI/UX design, AR, MR & VR development, smart city, 5G, IoT, big data,digital twin and machine learning/AI. Our industry focus is Health, Energy, Telecommunication, E-learning, Education, Industry 4.0, Agriculture, Automotive. Contact info: [email protected]
04/04/2023 NUROMEDIA GMBH Germany Expertise Request Reliable Services and Smart Security (HORIZON-JU-SNS-2023-STREAM-B-01-04) F&T portal
Nuromedia GmbH is a German software engineering & multimedia company with more than 15 years of experience in national and EU funded projects. Our team offers competences like software engineering, gamification, 2D/3D animation, UI/UX design, AR, MR & VR development, smart city, 5G, IoT, big data,digital twin and machine learning/AI. Our industry focus is Health, Energy, Telecommunication, E-learning, Education, Industry 4.0, Agriculture, Automotive. Contact info: [email protected]
03/04/2023 Consortium Ubiquitous Technologies S.c.a.r.l. Italy Expertise Request Global call according to SRIA 2023 (RIA) (HORIZON-KDT-JU-2023-2-RIA-TOPIC-1) F&T portal
Cubit is a SME specialised in design and realisation of custom electronic devices, monitoring & control systems, and Internet of Things (IoT) solutions for industrial automation, smart energy, smart city, environmental monitoring, wearables, tracking, etc. We can cover from hardware design to certification and industrialisation and software development including complex and intelligent algorithms on resource-constrained devices and integration with large systems and cloud platforms.
31/03/2023 MARKETKAPS S.A.S France Expertise Request AI-driven data operations and compliance technologies (AI, data and robotics partnership) (IA) (HORIZON-CL4-2024-DATA-01-01) F&T portal
LOOKING TO JOIN CONSORTIUM:
We are supporting the largest European investor in IT R&D and are looking for a consortium for this call. If you wish to know more, please contact me at [email protected]
Many thanks
François Le Page
Linkedin: https://www.linkedin.com/in/francoispierrelepage/
31/03/2023 NATIONAL UNIVERSITY OF LIFE AND ENVIRONMENTAL SCIENCES OF UKRAINE Ukraine Expertise Request Industrial leadership in AI, Data and Robotics boosting competitiveness and the green transition (AI Data and Robotics Partnership) (IA)[[https://www.europarl.europa.eu/RegData/etudes/STUD/2021/662906/IPOL_STU(2021)662906_EN.pdf]] (HORIZON-CL4-2024-DIGITAL-EMERGING-01-04) F&T portal
We are a scientific institution. We specialize in increasing the efficiency of operation of wheeled and tracked machines in the direction of reducing their impact on the environment (in agricultural production). We have experience in the design and construction of units and aggregates of mobile machines, increasing the efficiency of their operation, strength and reliability, maintenance and repair. Contact person: prof. Evgen Kalinin [email protected]