SMARTHEP Synergies between Machine leArning Real Time analysis and Hybrid arch...
SMARTHEP Synergies between Machine leArning Real Time analysis and Hybrid architectures for efficient Event Processing and decision making
SMARTHEP is a consortium formed by academic and industrial partners on scientific, technological, and entrepreneurship aspects of real-time analysis. The focus of SMARTHEP is a central question in a data-rich environment: how to m...
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Información proyecto SMARTHEP
Duración del proyecto: 52 meses
Fecha Inicio: 2021-05-03
Fecha Fin: 2025-09-30
Fecha límite de participación
Sin fecha límite de participación.
Descripción del proyecto
SMARTHEP is a consortium formed by academic and industrial partners on scientific, technological, and entrepreneurship aspects of real-time analysis. The focus of SMARTHEP is a central question in a data-rich environment: how to make the most of the available data to take decisions fast and efficiently, making the most of the available data. The main purpose of SMARTHEP is to train a new generation of inter-sector researchers and give them the tools to tackle this challenge, by processing large datasets in real- time, aided by Machine Learning and hybrid computing architectures. The results of SMARTHEP will benefit the HEP community in providing cutting edge technology and algorithms for the area of data selection (triggering) and particle detection, leading to precise measurement of the fundamental constituents of matter and enabling the discovery of new physics processes. The results of SMARTHEP include concrete commercial deliverables for industry in the fields of transport, finance and industrial decision-making processes. The training aspect of this work is crucial to SMARTHEP. The young investigators working within SMARTHEP will also be prepared as professionals in the upcoming and highly demanded area of Data Science. They will have the chance to gather work experiences and training in industry during their PhD degree and establish connections to companies, as well as acquire the skills that are necessary to a career in either industry or academia.