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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 NUROMEDIA GMBH Germany Expertise Request Coordination of the European software-defined vehicle platform (CSA) (HORIZON-KDT-JU-2023-3-CSA-TOPIC-3) 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]
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.
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 Ayuntamiento de Benetússer Spain Expertise Request Complementary SNS experimental Pan-EU federated Infrastructure (HORIZON-JU-SNS-2023-STREAM-C-01-01) F&T portal
Benetússer Town Council, which belongs to the metropolitan area of Valencia, has its own department of European projects. We work transversally with the different areas in the Town Council, such as equality, youth, participation, education, environment, urbanism, employment and development, among others, as well as with the civic organizations of the metropolitan area. We can add value and help increase the quality of European projects. If you are interested, contact [email protected]
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 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 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 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]
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 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 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 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]