BEGIN:VCALENDAR
VERSION:2.0
PRODID:-//MINERVA European Support Centre - ECPv6.18.2//NONSGML v1.0//EN
CALSCALE:GREGORIAN
METHOD:PUBLISH
X-ORIGINAL-URL:https://minerva4ai.eu
X-WR-CALDESC:Events for MINERVA European Support Centre
REFRESH-INTERVAL;VALUE=DURATION:PT1H
X-Robots-Tag:noindex
X-PUBLISHED-TTL:PT1H
BEGIN:VTIMEZONE
TZID:Europe/Rome
BEGIN:DAYLIGHT
TZOFFSETFROM:+0100
TZOFFSETTO:+0200
TZNAME:CEST
DTSTART:20250330T010000
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:+0200
TZOFFSETTO:+0100
TZNAME:CET
DTSTART:20251026T010000
END:STANDARD
BEGIN:DAYLIGHT
TZOFFSETFROM:+0100
TZOFFSETTO:+0200
TZNAME:CEST
DTSTART:20260329T010000
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:+0200
TZOFFSETTO:+0100
TZNAME:CET
DTSTART:20261025T010000
END:STANDARD
BEGIN:DAYLIGHT
TZOFFSETFROM:+0100
TZOFFSETTO:+0200
TZNAME:CEST
DTSTART:20270328T010000
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:+0200
TZOFFSETTO:+0100
TZNAME:CET
DTSTART:20271031T010000
END:STANDARD
BEGIN:DAYLIGHT
TZOFFSETFROM:+0100
TZOFFSETTO:+0200
TZNAME:CEST
DTSTART:20280326T010000
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:+0200
TZOFFSETTO:+0100
TZNAME:CET
DTSTART:20281029T010000
END:STANDARD
END:VTIMEZONE
BEGIN:VEVENT
DTSTART;TZID=Europe/Rome:20260616T100000
DTEND;TZID=Europe/Rome:20260616T110000
DTSTAMP:20260616T134629Z
CREATED:20260604T121053Z
LAST-MODIFIED:20260616T134629Z
UID:946-1781604000-1781607600@minerva4ai.eu
SUMMARY:MINERVA Webinar #5: Profiling AI workloads on GPUs – Identifying performance improvements
DESCRIPTION:The Webinar is for participants interested to learn how to identify and analyze performance improvements for AI workloads using profiling. Understand the fundamentals of profiling for AI workloads\, which can be further applied upon completion of the webinar. The webinar combines theoretical knowledge with guided coding walkthrough to help participants identify performance improvements\, understand traces\, and enhance efficiency of AI workloads on GPUs. \n\n\n\nIf you’re interested in how to use profiling to identify performance improvements for your AI workloads\, then this seminar is for you and/or if you’re working as e.g.\, a Data/AI scientist or as an engineer. \n\n\n\nAgenda \n\n\n\n\nIntroduction (5 min)\n\nWelcome & Project Introduction\n\n\n\nOverview of speaker role in AI and MINERVA\n\n\n\n\n\nIntroduction to Profiling (5 min)\n\n\n\nGetting Started with profiling (25 min)\n\n\n\nHow to profile AI workloads in practice (20min)\n\n\n\nClosing and Q&A Session (5 mins)\n\nOpen floor for questions\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nEnjoyed this event? Take our post training survey!
URL:https://minerva4ai.eu/event/minerva-webinar-5-profiling-ai-workloads-on-gpus-identifying-performance-improvements/
LOCATION:Italy
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Europe/Rome:20260707T160000
DTEND;TZID=Europe/Rome:20260707T170000
DTSTAMP:20260722T100005Z
CREATED:20260702T205840Z
LAST-MODIFIED:20260722T100005Z
UID:991-1783440000-1783443600@minerva4ai.eu
SUMMARY:MINERVA Online Journal Club #2: VL-JEPA: Joint Embedding PredictiveArchitecture for Vision-language
DESCRIPTION:We’ll return with the second installment of the online journal club. In the meeting\, we will have a look together at (VL-)JEPA [1]\, a model from Yann LeCun’s research agenda around Joint Embedding Predictive Architectures. Our team from Tübingen AI Center will host and lead the discussion. The session is open to all and free of charge. We encourage you to read the paper before joining\, but it’s not a strict requirement. If you have specific questions that you want to have discussed duringthe session\, send them to minerva@tuebingen.ai. \n\n\n\n(VL-)JEPA [1] is an approach to enhance vision-language models by introducing a predictive architecture. Instead of generating text tokens directly\, it predicts continuous embeddings representing abstractsemantic concepts. The intuition behind this is to have the model to focus on task-relevant meaning\, abstracting from surface-level linguistic variations\, leading to more efficient and semantically rich representations. \n\n\n\nLooking forward to see many of you online and diving deep into the research trends. \n\n\n\n[1] https://openreview.net/forum?id=tjimrqc2BU \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nRecordings
URL:https://minerva4ai.eu/event/minerva-online-journal-club-2-vl-jepa-joint-embedding-predictivearchitecture-for-vision-language/
LOCATION:Italy
ORGANIZER;CN="MINERVA":MAILTO:info@minerva4ai.eu
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Europe/Rome:20260804T160000
DTEND;TZID=Europe/Rome:20260804T170000
DTSTAMP:20260722T100257Z
CREATED:20260722T095659Z
LAST-MODIFIED:20260722T100257Z
UID:1034-1785859200-1785862800@minerva4ai.eu
SUMMARY:MINERVA Online Journal Club #3: Exploring Mamba-3
DESCRIPTION:The third instalment of the online journal club will feature the third instalment of the Mamba model series. We will discuss and dissect the ICLR 2026 paper “Mamba-3: Improved Sequence Modeling using State Space Principles” by Lahoti et al. [1]. Again\, the Tübingen AI Center team will host and lead the discussion. The session is open to all and free of charge. We encourage you to read the paper before joining\, but it’s not a strict requirement. If you have specific questions that you want to have discussed during the session\, send them to minerva@tuebingen.ai. \n\n\n\nMamba-3 [1] is the third instalment of an alternate LLM model architecture [2\, 3]. While the majority of transformer-based LLM architectures are structured as auto-regressive models\, mapping sequences of length n to sequences of length n+1\, models in the Mamba family capture the state of the model in a latent state\, thus circumventing the linear memory and quadratic compute cost at inference time. We will have a close look what implications this has both in terms of computational requirements\, but also in model performance and what future improvements might be expected from models in this family. \n\n\n\nLooking forward to see many of you online and diving deep into the research trends.  \n\n\n\n[1] https://openreview.net/forum?id=HwCvaJOiCj[2] https://openreview.net/forum?id=ztn8FCR1td[3] https://openreview.net/forum?id=tEYskw1VY2
URL:https://minerva4ai.eu/event/minerva-online-journal-club-3-mamba-3/
LOCATION:Italy
ORGANIZER;CN="T%C3%BCbingen AI Center":MAILTO:minerva@tuebingen.ai
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Europe/Rome:20260929T150000
DTEND;TZID=Europe/Rome:20260929T160000
DTSTAMP:20260903T143053Z
CREATED:20260903T143052Z
LAST-MODIFIED:20260903T143053Z
UID:1053-1790694000-1790697600@minerva4ai.eu
SUMMARY:MINERVA Webinar #6: HPC Coding Agents – 2nd workshop
DESCRIPTION:Description\n\n\n\nFollowing the previous workshop in April\, AMD Silo AI has refactored and open-sourced hpc-assistant for the HPC community and general audiences. The tool simplifies workflows across key areas: 📦 Easy Installation 📚 Knowledge Base Integration 📋 HPC Job Creation🛠️ Troubleshooting 📈 Performance Optimization 📊 Resource Management. In this presentation\, we will share the updates and demonstrate how to setup the assistant and run it with various HPC tasks. We will also cover the new energy profiling feature which is releasing soon. Code can be already accessed here: https://github.com/silogen/hpc-assistant   \n\n\n\nAgenda\n\n\n\n\n    Introduction (5 mins)\n\n    Welcome & Project Introduction from coordinator\n\n\n\n    Overview of speaker role in AI and MINERVA  \n\n\n\n\n\n    AMD Silo AI HPC Coding Agents (30 mins)\n\n    Updates on the AMD HPC Coding Agent  \n\n\n\n    Key capabilities and demo\, including energy profiling\n\n\n\n    Goals and future development outlook  \n\n\n\n\n\n    Closing and Q&A Session (15 mins)\n\n    Open floor for questions
URL:https://minerva4ai.eu/event/minerva-webinar-6-hpc-coding-agents-2nd-workshop/
LOCATION:Italy
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Europe/Rome:20261013T160000
DTEND;TZID=Europe/Rome:20261013T170000
DTSTAMP:20261005T103405Z
CREATED:20261005T102824Z
LAST-MODIFIED:20261005T103405Z
UID:1127-1791907200-1791910800@minerva4ai.eu
SUMMARY:MINERVA Online Journal Club #4: Raven
DESCRIPTION:We’re back after the summer break. In the fourth session\, we’ll discuss “Raven: High-Recall Sequence Modeling with Sparse Memory Routing” by Afzal et al. [1\, 2\, 3]. While the Tuebingen AI Center team will host the discussion\, we are proud and thankful to announce that the paper’s first author\, Arshia Afzal\, will join us for this installment. This session is open to all and free of charge. We encourage you to read the paper before joining\, but it’s not a strict requirement. If you have specific questions that you want to have discussed during the session\, send them to minerva@tuebingen.ai. \n\n\n\nLLMs’ arguably biggest strengths come from their ability to generate highly tailored output from their input sequence which is typically referred to as their context. In the last session\, we have seen how state space model LLMs like Mamba-3 use their latent state to store this context while “mainline” LLMs use a sliding window if the input length becomes too large. Raven analyzes the different approaches under a common mathematical framework and proposes a new context management strategy based on a learned update mapping. We’ll discuss how they’ve turned this insight into a language model and what other challenges they had to overcome during training and evaluation. \n\n\n\nLooking forward to see many of you online and diving deep into theresearch trends. \n\n\n\n[1] https://arxiv.org/abs/2607.25357 [2] https://arshiaafzal.github.io/blog/2026/raven-part1/[3]https://arshiaafzal.github.io/blog/2026/raven-part2/
URL:https://minerva4ai.eu/event/minerva-online-journal-club-4-raven/
LOCATION:Online
ORGANIZER;CN="T%C3%BCbingen AI Center":MAILTO:minerva@tuebingen.ai
END:VEVENT
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
END:VEVENT
END:VCALENDAR