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SUrvey Network for Deep Imaging Analysis and Learning

Though Big Data has become common in many domains nowadays, the challenges to develop efficient and automated mining of the ever increasing data sets by new generations of data scientists are eminent. These challenges span wide sw... ver más

30/09/2021

RIJKSUNIVERSITEIT...

4M€

Presupuesto del proyecto: 4M€

Líder del proyecto

RIJKSUNIVERSITEIT GRONINGEN No se ha especificado una descripción o un objeto social para esta compañía.

TRL 4-5

Fecha límite participación Sin fecha límite de participación.

Ver 16 Participantes

Financiación concedida El organismo H2020 notifico la concesión del proyecto el día 2021-09-30

MSCA-ITN-2016: Innovative Training Networks

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16 Participantes

RIJKSUNIVERSITEIT GRONINGEN

1.19M€ | Lider

ACIDEKA, S.A.

86.52K€ | Participante

VICOMTECH

N.D. | Participante

INAF

258.06K€ | Participante

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Duración del proyecto: 61 meses Fecha Inicio: 2016-08-12
Fecha Fin: 2021-09-30

Líder del proyecto

RIJKSUNIVERSITEIT GRONINGEN No se ha especificado una descripción o un objeto social para esta compañía.

TRL 4-5

Presupuesto del proyecto 4M€

Fecha límite de participación Sin fecha límite de participación.

Descripción del proyecto Though Big Data has become common in many domains nowadays, the challenges to develop efficient and automated mining of the ever increasing data sets by new generations of data scientists are eminent. These challenges span wide swathes of society, business and research. Astronomers with their high-tech observatories are historically at the forefront of this field, but obviously, the impact in e.g. commercial applications, security, environmental monitoring and experimental research is immense. We aim to contribute to this general discussion by training a number of young scientists in the fields of computer science and astronomy, focussing on techniques of automated learning from large quantities of data to answer fundamental questions on the evolution of properties of galaxies. While these techniques will lead to major advances in our understanding of the formation and evolution of galaxies, we will also promote, in collaboration with industry, much more general applications in society, e.g. in medical imaging or remote sensing. We have put together a team of astronomers and computer scientists, from academic and private sector partners, to develop techniques to detect and classify ultra-faint galaxies and galaxy remnants in a deep survey of the Fornax cluster, and use the results to study how galaxies evolve in the dense environment of galaxy clusters. With a team of young researchers we will develop novel computer science algorithms addressing fundamental topics in galaxy formation, such as the huge dark matter fractions inferred by theory, and the lack of detected angular momentum in galaxies. The collaboration is unique - it will develop a platform for deep symbiosis of two radically different strands of approaches: purely data-driven machine learning and specialist approaches based on techniques developed in astronomy. Young scientists trained with such skills are highly demanded both in research and business.The duration of the project was originally 48 months, till 31/3/2021. An extension of 6 months till 30/9/2021 was granted. The project therefore last 54 months which is indicated in all related project activities

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