| Issue |
EPJ Web of Conf.
Volume 295, 2024
26th International Conference on Computing in High Energy and Nuclear Physics (CHEP 2023)
|
|
|---|---|---|
| Article Number | 08022 | |
| Number of page(s) | 8 | |
| Section | Collaboration, Reinterpretation, Outreach and Education | |
| DOI | https://doi.org/10.1051/epjconf/202429508022 | |
| Published online | 06 May 2024 | |
https://doi.org/10.1051/epjconf/202429508022
The SMARTHEP European Training Network
1 Fakultät Physik, Technische Universität Dortmund, Dortmund, Germany
2 Département de Physique Nucléaire et Corpusculaire, Université de Genève, Geneva, Switzerland
3 Physikalisches Institüt, Ruprecht-Karls-Universität Heidelberg, Heidelberg, Germany
4 Department of Physics and Astronomy, University of Manchester, Manchester, United Kingdom
* e-mail: Jamie.Gooding@cern.ch
Published online: 6 May 2024
Synergies between MAchine learning, Real-Time analysis and Hybrid architectures for efficient Event Processing and decision-making (SMARTHEP) is a European Training Network, training a new generation of Early Stage Researchers (ESRs) to advance real-time decision-making, driving data-collection and analysis towards synonymity.
SMARTHEP brings together scientists from major LHC collaborations at the frontiers of real-time analysis (RTA) and key specialists from computer science and industry. By solving concrete problems as a community, SMARTHEP will further the adoption of RTA techniques, enabling future High Energy Physics (HEP) discoveries and generating impact in industry.
ESRs will contribute to European growth, leveraging their hands-on experience in machine learning and accelerators towards commercial deliverables in fields that can profit most from RTA, e.g., transport, manufacturing, and finance.
This contribution presents the training and outreach plan for the network, and is intended as an opportunity for further collaboration and feedback from the CHEP community.
© The Authors, published by EDP Sciences, 2024
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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