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X-WR-CALDESC:Events for MINERVA European Support Centre
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BEGIN:VEVENT
DTSTART;TZID=Europe/Rome:20261020T090000
DTEND;TZID=Europe/Rome:20261023T170000
DTSTAMP:20261005T120258Z
CREATED:20261005T115210Z
LAST-MODIFIED:20261005T120258Z
UID:1133-1792486800-1792774800@minerva4ai.eu
SUMMARY:Deep Learning optimized for Jean Zay (DLO-JZ)
DESCRIPTION:This training (held in French) is dedicated to the multi-GPU scaling of the training of a Deep Learning model. The common thread of the practical aspects of the training is the optimized scaling (acceleration\, distribution) of a model training on the ImageNet dataset\, in PyTorch. For this\, participants will be led to code and submit their calculations on Jean Zay applying the different concepts covered during the course sections. \n\n\n\nTraining details like: Prerequisites\, Duration and practical info\, Course content and Course materials are available in the event website link below.
URL:https://minerva4ai.eu/event/deep-learning-optimized-for-jean-zay-dlo-jz/
LOCATION:IDRIS-CNRS\, Rue John Von Neumann\, Building 506 BP 167\, ORSAY CEDEX\, 91403\, France
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Europe/Rome:20261116T000000
DTEND;TZID=Europe/Rome:20261118T235959
DTSTAMP:20260901T102358Z
CREATED:20260901T083712Z
LAST-MODIFIED:20260901T102358Z
UID:1044-1794787200-1795046399@minerva4ai.eu
SUMMARY:AI2F & MINERVA Hackathon: AI for Health 
DESCRIPTION:Driven by CINES’s growing involvement in the health sector and the rapid development of Artificial Intelligence in healthcare\, AI Factory France and MINERVA have joined forces to host a hackathon dedicated to AI for Healthcare.  \n\n\n\nHosted at CINES in Montpellier\, France\, this edition will bring together developers\, researchers\, clinicians\, data scientists\, and HPC/AI support teams to collaborate on real-world healthcare AI challenges. Participants will explore how to develop trustworthy AI solutions that combine performance\, explainability\, ethical principles\, and data sovereignty.  \n\n\n\nDuring this hackathon\, participating teams will have the opportunity to:  \n\n\n\n\nAddress healthcare data challenges and compliance requirements. \n\n\n\nDevelop trustworthy and explainable AI models for clinical and biomedical applications. \n\n\n\nOptimize AI training and inference workloads. \n\n\n\nScale applications accros multi-GPU and multi-node environnements. \n\n\n\nReceive hands-on support from experienced HPC and AI experts. \n\n\n\nExchange knowledge and best practices with teams from across the European computing community. \n\n\n\n\nParticipants are encouraged to bring their own AI project and benefit from expert guidance while running their workloads either on their own infrastructure or on some of Europe’s leading supercomputers\, including Adastra\, LUMI\, and Jean-Zay.  \n\n\n\nIf you have any questions\, please feel free to contact us at info@minerva4ai.eu.  \n\n\n\nApplications are open until 25 September\, EOB. To apply\, please complete the application form. 
URL:https://minerva4ai.eu/event/ai2f-minerva-hackathon-healthcare-ai/
LOCATION:CINES – Montpellier\, France\, 950 rue de St Priest\, Montpellier\, 34097\, France
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Europe/Rome:20270101T000000
DTEND;TZID=Europe/Rome:20270630T235959
DTSTAMP:20261005T121630Z
CREATED:20261005T121542Z
LAST-MODIFIED:20261005T121630Z
UID:1146-1798761600-1814399999@minerva4ai.eu
SUMMARY:Deep Learning optimized for Jean Zay (DLO-JZ) - English version
DESCRIPTION:This training (in English) is dedicated to the multi-GPU scaling of the training of a Deep Learning model. The common thread of the practical aspects of the training is the optimized scaling (acceleration\, distribution) of a model training on the ImageNet dataset\, in PyTorch. For this\, participants will be led to code and submit their calculations on Jean Zay applying the different concepts covered during the course sections. \n\n\n\nTraining details like: Prerequisites\, Duration and practical info\, Course content and Course materials are TBD. They will be available in the event website link below as soon as the actual dates are fixed (1st semester 2027).
URL:https://minerva4ai.eu/event/deep-learning-optimized-for-jean-zay-dlo-jz-2/
LOCATION:IDRIS-CNRS\, Rue John Von Neumann\, Building 506 BP 167\, ORSAY CEDEX\, 91403\, France
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Europe/Rome:20270223T000000
DTEND;TZID=Europe/Rome:20270224T235959
DTSTAMP:20261016T083641Z
CREATED:20261016T083125Z
LAST-MODIFIED:20261016T083641Z
UID:1155-1803340800-1803513599@minerva4ai.eu
SUMMARY:Minerva Conference
DESCRIPTION:AI on HPC \n\n\n\n\n\nThe MINERVA Conference brings together researchers\, engineers\, and infrastructure providers to explore how High Performance Computing (HPC) is enabling the next generation of AI. \n\n\n\n\n\nAbout the conference\n\n\n\nThe rapid growth of large-scale AI\, from foundation model pretraining to multimodal architecture\, increasingly relies on the computational power\, scalability\, and expertise found in High Performance Computing (HPC) centers. This conference brings together researchers\, engineers\, and infrastructure providers to explore the evolving relationship between AI and HPC\, showcasing how supercomputing resources are enabling the next generation of AI systems. \n\n\n\nOrganized by MINERVA\, a project designed to bring together HPC and AI communities\, the conference highlights the importance of collaboration between supercomputing centers\, research institutions\, and industry in advancing open and scalable AI innovation across Europe.
URL:https://minerva4ai.eu/event/minerva-conference/
LOCATION:DAMA Tecnopolo\, Via Stalingrado 84/3\, Bologna\, Italy\, 40128\, Italy
ATTACH;FMTTYPE=image/png:https://minerva4ai.eu/wp-content/uploads/2026/10/Designer2.png
ORGANIZER;CN="MINERVA":MAILTO:info@minerva4ai.eu
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Europe/Rome:20270323T090000
DTEND;TZID=Europe/Rome:20270326T173000
DTSTAMP:20261007T094435Z
CREATED:20261005T115944Z
LAST-MODIFIED:20261007T094435Z
UID:1141-1805792400-1806082200@minerva4ai.eu
SUMMARY:LLM specialization (SpeLLM)
DESCRIPTION:This training aims to equip participants with the essential skills to adapt and effectively use Large Language Models (LLMs). It covers the main model adaptation techniques\, including fine-tuning\, Prompt Engineering\, Retrieval-Augmented Generation (RAG)\, Parameter-Efficient Fine-Tuning (PEFT)\, and model alignment. The course also introduces multimodality and agentic systems\, two key concepts for designing advanced LLM-based applications. \n\n\n\nTraining details like: Prerequisites\, Duration and practical info\, Course content and Course materials are available in the event website link below.
URL:https://minerva4ai.eu/event/llm-specialization/
LOCATION:IDRIS-CNRS\, Rue John Von Neumann\, Building 506 BP 167\, ORSAY CEDEX\, 91403\, France
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