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Machine Sensing Training Network

Machine Sensing Training Network

Objective

The aim of this Innovative Training Network is to train a new generation of creative, entrepreneurial and innovative early-stage researchers (ESRs) in the research area of measurement and estimation of signals using knowledge or data about the underlying structure. With its combination of ideas from machine learning and sensing, we refer to this research topic as “Machine Sensing”. We will train all ESRs in research skills needed to obtain an internationally-recognized PhD; to experience applying their research a non-Academic sector; and to gain transferrable skills such as entrepreneurship and communication skills. We will further encourage an open “reproducible research” approach to research, through open publication of research papers, data and software, and foster an entrepreneurial and innovation-oriented attitude through exposure to SME and spin-out Partners in the network. In the research we undertake, we will go beyond the current, and hugely popular, sparse representation and compressed sensing approaches, to develop new signal models and sensing paradigms. These will include those based on new structures, nonlinear models, and physical models, while at the same time finding computationally efficient methods to perform this processing. We will develop new robust and efficient Machine Sensing theory and algorithms, together methods for a wide range of signals, including: advanced brain imaging; inverse imaging problems; audio and music signals; and non-traditional signals such as signals on graphs. We will apply these methods to real-world problems, through work with non-Academic partners, and disseminate the results of this research to a wide range of academic and non-academic audiences, including through publications, data, software and public engagement events.
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Coordinator

UNIVERSITY OF SURREY

Address

Stag Hill
Gu2 7xh Guildford

United Kingdom

Activity type

Higher or Secondary Education Establishments

EU Contribution

€ 546 575,76

Participants (9)

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INSTITUT NATIONAL DE RECHERCHE ENINFORMATIQUE ET AUTOMATIQUE

France

EU Contribution

€ 525 751,20

THE UNIVERSITY OF EDINBURGH

United Kingdom

EU Contribution

€ 546 575,76

TECHNISCHE UNIVERSITAET MUENCHEN

Germany

EU Contribution

€ 498 432,96

INSTITOUTO TECHNOLOGIAS YPOLOGISTONKAI EKDOSEON DIOFANTOS

Greece

EU Contribution

€ 484 773,84

INSTITUTO DE TELECOMUNICACOES

Portugal

EU Contribution

€ 476 712,72

TTY-SAATIO

Finland

TAMPEREEN KORKEAKOULUSAATIO SR

Finland

EU Contribution

€ 269 145,36

NOISELESS IMAGING OY

Finland

EU Contribution

€ 269 145,36

FRAUNHOFER GESELLSCHAFT ZUR FOERDERUNG DER ANGEWANDTEN FORSCHUNG E.V.

Germany

EU Contribution

€ 249 216,48

Partners (7)

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ECOLE POLYTECHNIQUE FEDERALE DE LAUSANNE

VisioSafe SA

GENERAL ELECTRIC DEUTSCHLAND HOLDING GMBH

Bioiatriki SA

Audio Analytic Ltd.

CEDAR Audio Ltd

Songquito UG (haftungsbeschränkt)

Project information

Grant agreement ID: 642685

Status

Closed project

  • Start date

    1 January 2015

  • End date

    31 December 2018

Funded under:

H2020-EU.1.3.1.

  • Overall budget:

    € 3 866 329,44

  • EU contribution

    € 3 866 329,44

Coordinated by:

UNIVERSITY OF SURREY

United Kingdom