ProjectWINDMILL
Machine learning to optimise future wireless communication
Integrating wireless communication engineering and machine learning (WINDMILL)
With the increasing number of Internet of Things devices being connected, there is a need for high-performing cellular networks, yet optimising network performance is becoming nearly impossible with traditional engineering approaches. The WINDMILL project trained a new generation of researchers to leverage the power of AI.
Project impact
Batteries are crucial technologies for the energy transition: they are the most expensive component of an electric vehicle, and they are important to stabilise a grid powered by solar panels and windmills. Yet so many questions are still being challenged: how can we reduce the amount of critical raw materials in a battery? How can we improve their performance and their safety? How can we best optimise their manufacturing process and how should they be recycled? Over the past five years, the DESTINY project has been training 50 PhD candidates in frontier battery innovation. This includes rethinking how we discover and engineer battery materials, developing new uses for smart batteries, and working out how to best apply them in industry. The training doesn’t stop at technology. The PhD candidates also learn about personal effectiveness, research management and entrepreneurship. The project’s goals fit with the European Union’s strategic efforts to strengthen Europe’s battery sector, as reflected in initiatives such as the European Battery Alliance. The innovations developed by this next generation of battery researchers and engineers will help to reduce the production costs of Europe’s battery industry and create new job opportunities.
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Coordinator
Denmark
Aalborg Universitet
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Participating and partners countries
- Finland
- Aalto Korkeakoulusaatio Sr
- France
- Eurecom GIE
- Nokia Networks France
- Germany
- Robert Bosch GmbH
- Italy
- Universita degli Studi di Padova
- Spain
- Centre Tecnologic de Telecomunicacions de Catalunya
- Worldsensing SL
- Sweden
- Ericsson AB
- Switzerland
- Eidgenössische Technische Hochschule Zürich
- France
- CentraleSupélec
- Institut National des Sciences Appliquées de Lyon
- Germany
- Intel Deutschland GmbH
- University of Stuttgart
- Norway
- Telenor ASA
- Spain
- Universitat Politècnica de Catalunya
- USA
- Mitsubishi Electric Research Laboratories Inc.
- Cornell University
- The University of Texas System
- Virginia Tech Applied Research Corporation
- DeepSig Inc.
- Finland