ProjektMINERVA – European Support Centre for Scalable AI Research and Deployment

Grunddaten

Akronym:
MINERVA
Titel:
European Support Centre for Scalable AI Research and Deployment
Laufzeit:
01.01.2025 bis 31.12.2027
Abstract / Kurz- beschreibung:
The MINERVA project focuses on advancing HPC-AI knowledge across European machine learning (ML) and AI communities, acting as a central hub for cutting-edge European competences in large-scale ML/AI research and development, including training, customization, management and related knowledge (e.g. datasets, procedures, compliance with regulation). At its core, MINERVA is focused on bridging the existing gaps in HPC utilization by ML/AI communities by facilitating the integration of cutting-edge HPC infrastructure in their workload thanks to a rich service portfolio that includes three different support levels, from support for porting AI applications and workflow on HPC infrastructures to support for the pre-training and customization of large-scale open foundation models. Indeed, special emphasis is placed on the support around research and development of open-source foundation models, as these can bolster competitiveness and foster innovation within the European digital ecosystem, ensuring the reuse of high quality models and datasets. MINERVA's comprehensive framework includes targeted training programs, availability of dataset, procedures and software stack in the participating HPC centres, best practice guides, interaction with the AI communities to collect and address their needs, as well as foster the development of a Community Hub that will bring together AI researchers, developers, users and HPC specialist with the aim of sharing knowledge and bring Europe at the forefront of the AI revolution. The project's outcomes are expected to significantly contribute to the strategic objectives of EuroHPC and the European Commission, positioning Europe as a leader in the digital age through the innovative application of HPC and AI technologies in scientific research, industry applications, and beyond.
Schlüsselwörter:
maschinelles Lernen
machine learning
künstliche Intelligenz
artificial intelligence
High Performance Computing
Large AI Models

Beteiligte Mitarbeiter/innen

Leiter/innen

Mathematisch-Naturwissenschaftliche Fakultät
Universität Tübingen
Institut für Theoretische Physik (ITP)
Fachbereich Physik, Mathematisch-Naturwissenschaftliche Fakultät
SFB 1233 - Robustheit des Sehens – Prinzipien der Inferenz und der neuronalen Mechanismen
Sonderforschungsbereiche und Transregios
Bernstein Center for Computational Neuroscience Tübingen (BCCN)
Interfakultäre Institute
Tübingen AI Center
Fachbereich Informatik, Mathematisch-Naturwissenschaftliche Fakultät

Weitere Mitarbeiter/innen

Institut für Theoretische Physik (ITP)
Fachbereich Physik, Mathematisch-Naturwissenschaftliche Fakultät
Institut für Theoretische Physik (ITP)
Fachbereich Physik, Mathematisch-Naturwissenschaftliche Fakultät
Institut für Theoretische Physik (ITP)
Fachbereich Physik, Mathematisch-Naturwissenschaftliche Fakultät
Fachbereich Informatik
Mathematisch-Naturwissenschaftliche Fakultät

Lokale Einrichtungen

Tübingen AI Center
Fachbereich Informatik
Mathematisch-Naturwissenschaftliche Fakultät
Institut für Theoretische Physik (ITP)
Fachbereich Physik
Mathematisch-Naturwissenschaftliche Fakultät

Geldgeber

Brüssel, Belgien
Bonn, Nordrhein-Westfalen, Deutschland
Hilfe

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