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DTSTART;TZID=Europe/Stockholm:20260603T110000
DTEND;TZID=Europe/Stockholm:20260603T123000
DTSTAMP:20260610T071050
CREATED:20260507T103302Z
LAST-MODIFIED:20260508T043926Z
UID:10000020-1780484400-1780489800@mimer-ai.eu
SUMMARY:From Lab Data to AI-Ready Insights: An Introduction to NOMAD for Materials Science Researchers
DESCRIPTION:About the webinar\nResearch in materials science generates vast amounts of experimental and computational data\, but much of it remains siloed\, poorly documented\, or difficult to reuse. This webinar introduces the NOMAD platform\, a free\, open-source research data management ecosystem developed by the FAIRmat consortium\, designed specifically for the materials science community. \nNOMAD enables researchers and R&D teams to store\, structure\, share\, and publish their data following the FAIR principles (Findable\, Accessible\, Interoperable\, and Reusable)\, thus transforming raw outputs into structured\, high-quality datasets ready for collaboration and machine learning applications. For organisations that require full control over their data\, NOMAD Oasis offers a self-hosted deployment that brings the same powerful capabilities to a private\, secure environment\, which makes it equally relevant for industrial R&D settings. \nWho is the webinar for?\nWhether you are a lab leader thinking about your group’s data strategy\, an experimentalist looking to better organise your measurements\, a computational researcher interested in making simulation data more reusable and AI-ready\, or an industry professional exploring smarter data management for your R&D pipeline — this webinar offers a practical and accessible starting point. No prior experience with research data management tools required. \nKey takeaways for participants\n\n\nUnderstand the FAIR principles and why structured data management matters for modern research and R&D. \n\n\nGet a clear overview of the NOMAD ecosystem: including the central NOMAD repository\, Electronic Lab Notebooks (ELNs)\, and NOMAD OASIS/CAMELS? \n\n\nSee concrete examples of NOMAD applied to materials synthesis\, characterization\, and simulation data\, and how outputs from common instruments and codes can be automatically parsed and structured. \n\n\nUnderstand the bridge to machine learning: how properly managed\, FAIR-structured data becomes the foundation for AI-driven discovery and accelerated materials development. \n\n\nLearn about NOMAD Oasis as a self-hosted\, private deployment option — particularly relevant for organisations and companies that need to retain full control over their data. \n\n\nKnow the first practical steps your group or team can take to get started\, and where to find tutorials\, documentation\, and community support. \n\n\nSpeaker bio\nDr. Hampus Näsström is a materials data scientist at FAIRmat\, the NFDI consortium dedicated to making materials science data FAIR and reusable. He completed his PhD at Humboldt University of Berlin\, where his research focused on combinatorial synthesis of solar cell materials with an emphasis on high-throughput experimentation and lab automation\, which gives him a rare combination of hands-on experimental expertise and deep knowledge of data infrastructure. \nAt FAIRmat\, Hampus is a core contributor to the NOMAD platform\, including the development of data schemas and parsers for experimental measurement data such as X-ray diffraction and thin film characterization. He has led NOMAD tutorial tours and training events across Germany\, bringing the platform to local research communities through overview talks and hands-on workshops. His work at the intersection of experimental materials science\, research data management\, and AI-readiness makes him an ideal guide for research groups taking their first steps toward data-driven science.
URL:https://mimer-ai.eu/event/introduction-to-nomad-for-materials-science-researchers/
LOCATION:Online
ATTACH;FMTTYPE=image/webp:https://mimer-ai.eu/wp-content/uploads/2026/05/NOMAD-webinar.webp
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Europe/Stockholm:20260527T100000
DTEND;TZID=Europe/Stockholm:20260527T113000
DTSTAMP:20260610T071050
CREATED:20260316T103842Z
LAST-MODIFIED:20260316T120826Z
UID:10000015-1779876000-1779881400@mimer-ai.eu
SUMMARY:Advanced image analysis and AI/ML for medical imaging
DESCRIPTION:About the webinar\nThis webinar will present Artificial Intelligence for CT medical imaging\, focusing on automated methods for large-scale body composition analysis. The presentation will introduce deep learning techniques for image segmentation\, image registration and deep regression methodes of CT images\, enabling detailed assessment of tissues such as muscle\, adipose tissue\, and organs. \nParticipants will gain insights how AI can be used to automatically analyze CT scans for body composition\, enabling research in metabolic diseases and population studies\, and explore challenges and future directions in applying AI to large-scale body composition analysis. \nWho is the webinar for?\n\n\nResearchers working in medical imaging\, AI\, or data science \n\n\nPhD students and academics interested in AI for healthcare \n\n\nData scientists and machine learning engineers working with medical data \n\n\nClinicians and radiologists interested in AI-assisted image analysis \n\n\nKey takeaways for participants:\n\n\nHow AI and deep learning enable automated analysis of CT medical images \n\n\nMethods for large-scale body composition analysis \n\n\nHow medical imaging data can be used to study metabolic and health-related conditions \n\n\nChallenges and future directions in AI-driven medical imaging research \n\n\nSpeaker bio:\nNouman Ahmad holds a Ph.D. in Medical Science (Data Science) from Uppsala University\, Sweden. His research focuses on developing artificial intelligence methods for medical image segmentation\, registration\, and quantitative analysis of CT imaging data. His work centers on analyzing large-scale medical imaging datasets to better understand body composition and metabolic diseases. \nMore events and learning\nVisit our events and learning page to find more training possibilities.
URL:https://mimer-ai.eu/event/advanced-image-analysis-and-ai-ml-for-medical-imaging/
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Europe/Stockholm:20260526T090000
DTEND;TZID=Europe/Stockholm:20260529T120000
DTSTAMP:20260610T071050
CREATED:20260512T115439Z
LAST-MODIFIED:20260512T115439Z
UID:10000021-1779786000-1780056000@mimer-ai.eu
SUMMARY:Julia for High Performance Data Analysis
DESCRIPTION:This event is organised by: Mimer AI Factory & ENCCS  \nOverview\nJulia is a modern high-level programming language that is fast (on par with traditional HPC languages like Fortran and C) and relatively easy to write like Python or Matlab. It thus solves the two-language problem\, i.e. when prototype code in a high-level language needs to be combined with or rewritten in a lower-level language to improve performance. Although Julia is a general-purpose language\, many of its features are particularly useful for numerical scientific computation\, and a wide range of both domain-specific and general libraries are available for statistics\, machine learning\, and numerical modeling. \nJoin us for Julia for High Performance Data Analysis\, a hands-on workshop designed to equip you with practical skills for working with large datasets\, optimizing code\, and leveraging Julia’s rich ecosystem of libraries. You’ll explore real-world applications in data analysis\, numerical computation\, and machine learning\, all while discovering how Julia can streamline your workflow and elevate your performance without sacrificing code readability. \nWho is this workshop for?\nThis workshop is aimed at students\, researchers\, and developers who: \n\nAre already familiar with one or more programming languages such as Julia\, Python\, R\, C/C++\, Fortran\, or Matlab.\nWork with large datasets or need to perform computationally intensive modeling and analysis.\nWant to develop high-performance data science applications while staying within a productive\, high-level programming environment.\n\nPrerequisites\n\nExperience with one or more programming languages.\nFamiliarity with basic concepts in linear algebra and machine learning.\nBasic experience working in a terminal is helpful.\n\nKey takeaways\nThis online workshop will start by briefly covering the basics of Julia’s syntax and features\, and then introduce methods and libraries which are useful for writing high-performance code for modern HPC systems. After attending the workshop\, you will: \n\nBe comfortable with Julia’s syntax\, built-in package manager\, and development tools.\nUnderstand core language features like its type system\, multiple dispatch\, and composability.\nBe able to write your own Julia packages from scratch.\nKnow how to perform various linear algebra analysis on datasets.\nBe productive in analyzing and visualizing large datasets in Julia using dataframes and visualization packages.\nBe familiar with several Julia libraries for visualization and machine learning.\nUnderstand how to analyze large datasets efficiently in Julia using statistical methods.\n\nTentative Agenda\n\n\n\n\nTime (9:00-12:00) (CET)\nContents\n\n\n\n\nMay 26\nMotivation\, julia syntax\, special Julia features\, developing in Julia\, package ecosystem\n\n\nMay 27\nMotivation (julia for data analysis)\, data formats and dataframes\, linear algebra\, machine learning (data part)\n\n\nMay 28\nMachine learning\, clustering and classification\, deep learning\n\n\nMay 29\nNon-linear regression\, scientific machine learning\, conclusions and outlook
URL:https://mimer-ai.eu/event/julia-for-high-performance-data-analysis/
ATTACH;FMTTYPE=image/webp:https://mimer-ai.eu/wp-content/uploads/2026/05/Mimer-workshop.webp
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Europe/Stockholm:20260429T090000
DTEND;TZID=Europe/Stockholm:20260429T150000
DTSTAMP:20260610T071050
CREATED:20260305T141654Z
LAST-MODIFIED:20260422T102942Z
UID:10000012-1777453200-1777474800@mimer-ai.eu
SUMMARY:Hands-on Kubernetes as a User
DESCRIPTION:Registration is closed for this event\nAbout the event\nThis training is organized in collaboration with ENCCS.  \nKubernetes is the backbone of modern infrastructure\, being used for deployment of all sorts of applications. Nowadays\, knowing how to use it is an important skillset for those interested in artificial intelligence\, data science\, and MLOps. \nIn this highly practical workshop\, you will learn how to use Kubernetes from a user perspective. The course combines short theoretical introductions with guided hands-on exercises where you will configure a local cluster in your computer\, deploy services\, and run real workloads. By the end of the workshop\, you will have a working environment on your own machine and practical experience deploying interactive tools\, monitoring solutions\, and AI workloads. \nWho is this for?\nThis workshop is intended for: \n\n\nAI/MLOps engineers\, Researchers\, data scientists working with AI\, machine learning\, data science\, or scientific computing \n\n\nUsers of HPC or cloud environments who want to understand Kubernetes-based platforms and deploy their own stack without waiting for an infrastructure team. \n\n\nAnyone interested in MLOps\, reproducible workflows\, and scalable application deployment \n\n\nBeginners to Kubernetes who want a practical\, user-level introduction \n\n\nNo prior Kubernetes experience is required. \nKey takeaways for participants\n\n\nUnderstand what Kubernetes is\, its architecture\, core components\, and different deployment flavors\, including how it is used in multi-tenant environments (e.g.\, LUMI-K) \n\n\nLearn important Kubernetes concepts such as namespaces\, pods\, deployments\, jobs\, storage\, and scaling \n\n\nUnderstand the Kubernetes networking model\, including services\, ingress\, and load balancing \n\n\nBe able to read\, write\, and modify YAML manifests to deploy and manage applications \n\n\nDeploy and use JupyterLab for interactive computing inside a Kubernetes cluster \n\n\nSet up Prometheus and Grafana to monitor cluster resources and workloads \n\n\nDeploy a simple AI model and quickly track experiments using MLflow \n\n\nGain practical experience working with a local Kubernetes cluster on your own machine \n\n\nPrerequisites\n\n\nParticipants should: \n\n\nHave a laptop with Windows (WSL)\, macOS\, or Linux \n\n\nHave administrative privileges on the system \n\n\nInstall the required tools before the workshop (instructions will be provided) \n\n\nBasic knowledge of containers (e.g.\, Docker) is helpful but not required. Relevant concepts will be introduced during the workshop. \n\n\nNotes\nThis is a 5-hour hands-on workshop focused on practical usage. The course will not cover: \n\n\nKubernetes API development \n\n\nCluster installation or administration in production environments \n\n\nAdvanced operational or DevOps topics \n\n\nThe goal is to provide a solid user-level foundation for running applications and AI workloads on Kubernetes. \nSchedule\n\n\n\n28 April 2026\nContents\n\n\n\n\n13:00 – 16:00\nConfiguring a local cluster in your computer (Optional)\n\n\n\n\n29 April 2026\n\n\n\n\n\n09:00 – 09:40\nIntroduction to Kubernetes: Architecture\, Components and Flavors (Theory)\n\n\n09:40 – 10:50\nIntroduction to Kubernetes: Concepts (Theory)\n\n\n10:50 – 11:00\nShort Break\n\n\n11:00 – 12:00\nHands-on 1: Deploying Jupyterhub\, Prometheus and Grafana\n\n\n12:00 – 13:15\nLunch break\n\n\n13:15 – 14:45\nHands-on 2: Deploying an AI model and MLflow\n\n\n14:45 – 15:00\nQ&A session\n\n\n\n 
URL:https://mimer-ai.eu/event/hands-on-kubernetes-as-a-user/
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Europe/Stockholm:20260423T090000
DTEND;TZID=Europe/Stockholm:20260423T120000
DTSTAMP:20260610T071050
CREATED:20260413T114338Z
LAST-MODIFIED:20260413T114907Z
UID:10000016-1776934800-1776945600@mimer-ai.eu
SUMMARY:Discover what Mimer AI Factory offers
DESCRIPTION:Register latest by: 20 April 2026\nCurious how AI and supercomputing can boost your work?\nWe are pleased to invite you to meet and learn about the Mimer AI Factory\, 23 of April at 9-12 in Polhemssalen (10134) at the Ångström Laboratory. The Mimer AI Factory is part of an EU program that builds a network of supercomputing-powered hubs across Europe to accelerate the development of trustworthy\, cutting-edge AI models and support innovation for researchers\, startups\, and industry. Through Mimer\, you can receive: \n\nAccess to high-performance compute resources (GPU and CPU)\nTechnical support from AI experts across multiple domains\nGuidance on scaling\, optimisation\, and validation of AI solutions\n\nWe warmly invite you to join and take part in this kick-off (see program below) to explore how Mimer can create value for your AI initiatives. \nSchedule\n\n\n\n15 April 2026\nContents\nSpeaker\n\n\n\n\n09:00-09:15\nMimer AI Factory\nRossen Apostolov\n\n\n09:15-09:45\nSuccess Stories from Different Domains:\n– Material Science\n– Life Science\n– Autonomous System\n– Gaming\n– Trustworthy AI\nMarzieh Saeedimasine & Depeng Chen\nFatemeh Rahimian\nSima Sinaei\nBjörn Flintbeg\nNishat I Mowla\n\n\n10:00-10:30\nAI Research at Uppsala University:\n– Life Science\n– Materials Science\nOla Spjuth\nDepartment of Pharmaceutical BiosciencesJonathan Scragg\nMaterials Science and Engineering\n\n\n10:30 – 12:00\nPanel discussion – How can Mimer help you?\nRossen Apostolov
URL:https://mimer-ai.eu/event/mimer-ai-factory-kickoff-off-plenary/
ATTACH;FMTTYPE=image/webp:https://mimer-ai.eu/wp-content/uploads/2026/04/Kickoff.webp
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Europe/Stockholm:20260421T140000
DTEND;TZID=Europe/Stockholm:20260421T153000
DTSTAMP:20260610T071050
CREATED:20260312T142620Z
LAST-MODIFIED:20260312T145139Z
UID:10000013-1776780000-1776785400@mimer-ai.eu
SUMMARY:Scientific Machine Learning applied to Neuroscience
DESCRIPTION:About the webinar\nScientific Machine Learning is a rapidly evolving field that combines machine learning\, artificial intelligence\, and traditional scientific computing. Its application in Neuroscience is at the forefront of this field\, bridging the gap between classical computational modeling and state-of-the-art AI. These applications range from replacing traditional partial differential equation solvers with neural surrogates to evaluating the computational complexity of single neurons. In this talk\, we will examine some of these methodologies\, highlighting their inherent strengths and limitations\, as well as the emerging pathways being defined within this growing field. \nWho is the webinar for?\nMaster’s and PhD students\, researchers\, professors\, and anyone with a basic understanding of AI and machine learning. \nKey takeaways for participants:\n\n\nUnderstanding the strengths and weaknesses of neural surrogates in Computational Neuroscience. \n\n\nIdentifying emerging pathways in the rapidly growing field of Scientific Machine Learning applied to Computational Neuroscience. \n\n\nSpeaker bio:\nLuca Pellegrini is a PhD student in the Joint PhD Program in Computational Mathematics and Decision Sciences at the University of Pavia (UniPv) and the University of Lugano (USI). His research focuses on applying neural networks to computational electrophysiology. In particular\, he focuses on exploring Scientific Machine Learning methods\, such as neural operators and physics-informed neural networks\, to solve stiff ionic problems. He also works on reproducing the input-output mapping of Purkinje cells through causality-respecting networks. Additionally\, he investigates hybrid methods that combine neural networks with classical numerical solvers to leverage the strengths of neural networks with classical numerical solvers. \nLinkedin: www.linkedin.com/in/pellegrini-luca
URL:https://mimer-ai.eu/event/scientific-ml-applied-to-neuroscience/
LOCATION:Online
ATTACH;FMTTYPE=image/jpeg:https://mimer-ai.eu/wp-content/uploads/2026/03/Mimer-webinarr.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Europe/Stockholm:20260415T110000
DTEND;TZID=Europe/Stockholm:20260415T123000
DTSTAMP:20260610T071050
CREATED:20260224T074823Z
LAST-MODIFIED:20260306T135522Z
UID:10000011-1776250800-1776256200@mimer-ai.eu
SUMMARY:Operationalizing AI: MLOps x LLMOps
DESCRIPTION:About the webinar\nIn this MLOps and LLMOps webinar\, we’ll walk through the entire AI lifecycle – from idea and experimentation to production\, deployment and continuous monitoring\, highlighting how AI differs from traditional software (data-driven\, non-linear\, and sometimes unpredictable even when “done right”). You’ll learn the main deployment patterns (batch/offline\, real-time/online\, and common patterns employed in cloud solutions) and the key trade-offs around latency\, scaling\, and operational reliability. \nWe’ll then connect MLOps and LLMOps in a practical way: versioning data/models/prompts\, reproducibility\, CI/CD\, and testing strategies for probabilistic systems. It’s aimed at data scientists\, ML engineers\, software engineers\, and AI engineers who want a clear\, production-focused view of how to run ML and LLM solutions end-to-end. \nWho is the webinar for? \nIt’s aimed at data scientists\, ML engineers\, software engineers\, and AI engineers who want a clear\, production-focused view of how to run ML and LLM solutions end-to-end. Also suited to those with no experience in building and deploying AI models\, and are curious on AI/ML/LLM Ops. \nKey takeaways for participants: \n\n\nKey differences between AI and traditional software \n\n\nHow these differences translate to model deployment \n\n\nWhat is ML and LLM Ops and how they differ \n\n\nDifferent model deployment strategies \n\n\nSpeaker bio:\nMurilo Kuniyoshi Suzart Cunha (https://www.linkedin.com/in/murilo-cunha/) \nMurilo is a machine learning engineer specializing in productionizing models and applying AI Ops best practices\, with a focus on the evolving landscape of LLMOps. He takes a pragmatic approach to machine learning\, ensuring AI initiatives deliver tangible ROI. An experienced international conference speaker and open source supporter\, Murilo is also the host of the Monkey Patching Podcast.
URL:https://mimer-ai.eu/event/operationalizing-ai-mlops-x-llmops/
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Europe/Stockholm:20260325T170000
DTEND;TZID=Europe/Stockholm:20260325T200000
DTSTAMP:20260610T071050
CREATED:20260313T133240Z
LAST-MODIFIED:20260313T211947Z
UID:10000014-1774458000-1774468800@mimer-ai.eu
SUMMARY:Mimer hackathon
DESCRIPTION:​About the hackathon\nJoin our hackathon to learn how to use “research AI agents” using free European infrastructure. \nWe are pleased to collaborate with ​Stockholm AI and bring you this mini hackathon that will utilize Mimer’s free AI infrastructure. \n​The agenda is as follows: \n\n​17:15 Doors open + mingle\n18:00 Introduction to Mimer and the hackathon task. The task will be to create “research agents” using Mimer and open weight models that can answer questions using Sweden’s public data (e.g. SCB).\n​18:30 Hackathon begins\n​20:00 Wrap-up presentations and demos\n​20:30 Home time\n\n​Food and beverage will be provided. \n​NOTE: While the models will be served on Mimer infra\, people are still expected to bring their laptops to participate. You should have experience in setting up and running Python applications\,.e.g using uv\, calling APIs\, etc. You are of course free to use your favourite coding agent/IDE of choice. \nVenue\nDrottning Kristinas väg 61\, Stockholm
URL:https://mimer-ai.eu/event/agent-mimer-hackathon/
LOCATION:Drottning Kristinas väg 61\, Stockholm
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Europe/Stockholm:20260317T100000
DTEND;TZID=Europe/Stockholm:20260326T150000
DTSTAMP:20260610T071050
CREATED:20260223T103811Z
LAST-MODIFIED:20260306T131947Z
UID:10000010-1773741600-1774537200@mimer-ai.eu
SUMMARY:CodeRefinery workshop on coding tools and techniques for reproducible research
DESCRIPTION:About the course\n\n\n\nAre you writing code for your research? Do you want to make your research results more reproducible? Do you struggle to reproduce results of your own or others computations? Join the CodeRefinery workshop on coding tools and techniques. \n🗓️ The workshop runs on March 17–19 and March 24–26\, offering three consecutive days each week to maximize learning and networking opportunities. 📍Hjärne and Lallerstedt\, KTH Library and online \n\n\n\n\n\nThe intended audience for this workshop are researchers of all domains\, levels and preferred programming languages who write code in their research\, and the aim is to improve the reproducibility of our research by deepening the knowledge of the tools that enable better code development and sharing. \n\n\n\nThe workshop is held online (streamed on Twitch) with hands-on sessions. \n\n\n\nThe event is free of charge. More info and registration on the CodeRefinery Workshop site. \n\n\n\nIn-person hybrid event\n\n\n\nIn addition to the option to participate online\, this edition of the workshop also offers limited seats to a local exercise group at KTH library for participants in Sweden. \n\n\n\nSecure your spots here: \n\n\n\n\nRegistration for week 1\n\n\n\nRegistration for week 2\n\n\n\n\nDuring the CodeRefinery workshop on coding tools and techniques you will get the opportunity to interact with trainers from Mimer\, ENCCS\, KTH and other partner organizations.
URL:https://mimer-ai.eu/event/coderefinery-workshop-on-coding-tools-and-techniques-for-reproducible-research/
LOCATION:Hjärne and Lallerstedt\, KTH Library and online
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Europe/Stockholm:20260303T110000
DTEND;TZID=Europe/Stockholm:20260303T120000
DTSTAMP:20260610T071050
CREATED:20260202T082811Z
LAST-MODIFIED:20260202T082811Z
UID:10000009-1772535600-1772539200@mimer-ai.eu
SUMMARY:Secure coding for data scientists
DESCRIPTION:About the webinar\nWe give an overview of secure coding practices and guidelines. What should we keep in mind while writing code so as to mitigate security concerns. We will mention and explain some of the most common vulnerabilities is code and exemplify from a data science perspective. \nWho is the webinar for?\nDevelopers and code producers who work with Python and data science and need to take security risks into account when coding. Beginners in cyber security and secure coding. \nKey takeaways for participants:\n\nWhat are common vulnerabilities in code and mistakes or bad practices?\nHow to write safe code in some typical cases.\nWhat are some useful resources such as guidelines and standards for secure coding?\n\nSpeaker bio:\nDavid Eklund is an AI researcher at RISE working with mathematical modeling as well as privacy and security of AI models. David has a PhD in mathematics from KTH the Royal Institute of Technology. He is one of the training coordinators of the MIMER AI factory. \nAbdul Ghafoor is an Application Security Specialist with over a decade of experience in secure application development and vulnerability assessment. He holds a Ph.D. in Application Security from KTH Royal Institute of Technology (Sweden)\, where his research focused on secure-by-design applications\, shift-left security\, and the use of AI in cybersecurity.
URL:https://mimer-ai.eu/event/secure-coding-for-data-scientists/
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Europe/Stockholm:20260216T090000
DTEND;TZID=Europe/Stockholm:20260219T120000
DTSTAMP:20260610T071050
CREATED:20260109T090652Z
LAST-MODIFIED:20260109T122441Z
UID:10000006-1771232400-1771502400@mimer-ai.eu
SUMMARY:Introduction to Deep Learning
DESCRIPTION:Register latest by: 12 Feb 2026\nAbout the event\nThis event is organised by: Mimer AI Factory & LUMI AI Factory (CSC) \nDeep learning is a powerful subset of machine learning where computers learn patterns from data\, similar to how our brains learn. It uses artificial neural networks – systems inspired by biological neurons that process information through many layers. The term “deep” refers to networks with tens or hundreds of layers\, each containing millions of connections. Deep learning today powers technologies ranging from foundational applications such as language models and image recognition\, to cutting edge applications such as weather forecasting and protein folding. However\, for beginners\, stepping into this field can feel daunting and we intend to make this easy for you. \nThis online workshop\, organized by Mimer\, in partnership with LUMI AI Factory\, is designed to provide a beginner-friendly introduction to deep learning concepts\, workflows\, architectures\, and practical applications. You will learn end-to-end approaches for: \n\n\ntackling AI tasks including classification and regression \n\n\nbuilding deep model architectures such as Convolutional Neural Networks (CNNs) \n\n\napply advanced training techniques such as transfer learning \n\n\nWho is this for? \n\nStudents and early-career researchers in computer science\, bioinformatics\, natural sciences\, engineering\, or related fields\nData scientists working on deep learning-based applications\nAspiring software developers looking to learn foundational skills in AI\n\nKey takeaways for participants: \nA gentle introduction to deep learning fundamentals\, covering: \n\nCore concepts and terminology\n\nSteps in a deep learning workflow using Python and Keras (with Tensorflow\, and potentially also with PyTorch as backend) \n\nData preparation for training\nImplementing a basic neural network\nMonitoring and troubleshooting the training process\nVisualizing results and evaluating model performance\n\nPrerequisites: \n\n\n\nBasic Python programming skills and familiarity with packages like NumPy\, Pandas\, and Matplotlib\nExperience working with Jupyter notebooks (recommended but not mandatory)\n\n\n\nSchedule\nAll times in CET (Europe/Stockholm time) \n\n\n\nDay\nTime\nContents\n\n\n\n\n2026-02-16\n9:00 – 11:00\nSetup and dry-run\n\n\n2026-02-17\n9:00 – 12:00\nIntroduction;\nClassification by a neural network using Keras\n\n\n2026-02-18\n9:00 – 12:00\nMonitoring the training process\n\n\n2026-02-19\n9:00 – 12:00\nAdvanced Layer types; Transfer learning\n\n\n\nInstructors and Teaching Assistants\n\nAshwin Mohanan\, Mimer AIF / RISE\nFrancesco Fiusco\, Mimer AIF / RISE\nKatja Mankinen\, LUMI AIF / CSC\nLodovico Giaretta\, Mimer AIF / RISE\nMarlon Tobaben\, LUMI AIF / CSC\nOskar Taubert\, LUMI AIF / CSC\nYonglei Wang\, Mimer AIF / NAISS
URL:https://mimer-ai.eu/event/introduction-to-deep-learning/
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BEGIN:VEVENT
DTSTART;TZID=Europe/Stockholm:20260211T113000
DTEND;TZID=Europe/Stockholm:20260211T133000
DTSTAMP:20260610T071050
CREATED:20251120T135457Z
LAST-MODIFIED:20260109T105307Z
UID:10000005-1770809400-1770816600@mimer-ai.eu
SUMMARY:Healthcare foundation models
DESCRIPTION:| Speaker: Ludvig Hult\, Uppsala University \nAbout the Webinar\nAll of society is in rapid change as AI becomes more prolific. Generative models for text and images have disrupted sectors like marketing\, publishing and software. Healthcare seems to be lagging behind\, largely because many obstacles for AI in healthcare are poorly addressed by well known commercial providers like ChatGPT and Gemini. \nThis webinar highlights the unique challenges in healthcare\, such as data security and model safety. Topics covered include applications of AI in healthcare (transcription\, summarization)\, and key challenges (data privacy\, algorithmic fairness). \nThe EmergAI project at Uppsala University explores other types of AI to face these challenges. One of those models\, the healthcare event foundation model is introduced\, which can be thought of as a specialized LLM. Other approaches such as use of multimodal models in this context are also presented. \nWho is the webinar for\nThis talk is ideal for healthcare professionals\, data scientists\, researchers in life sciences\, and those in related fields. Additionally\, it offers inspiration to anyone with a general interest in AI and machine learning\, highlighting the importance of developing specialized models to ensure safe and reliable systems. \nKey takeaways for participants:\n\nExplore how AI can be employed in transcription\, summarization\, and image recognition within healthcare settings.\nUnderstand the key challenges facing AI implementation in healthcare\, including data limitations\, security concerns\, safety issues\, fairness considerations\, and compliance with Medical Device Regulations (MDR).\nDiscover EmergAI’s innovative approach through the healthcare event foundation model\, multimodal models and other specialized models\, including applications in ECG analysis and self-reported data.\nUnderstand the importance of tailored AI models like sequence models in healthcare and how they compare to generic large language models (LLMs).\n\n 
URL:https://mimer-ai.eu/event/healthcare-foundation-models/
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BEGIN:VEVENT
DTSTART;TZID=Europe/Stockholm:20260122T110000
DTEND;TZID=Europe/Stockholm:20260122T123000
DTSTAMP:20260610T071050
CREATED:20251120T071656Z
LAST-MODIFIED:20260123T092528Z
UID:10000003-1769079600-1769085000@mimer-ai.eu
SUMMARY:Resource-Efficient AI Model Parallelisation on LUMI Supercomputer
DESCRIPTION:| Speaker: Dr. Vijeta Sharma \nAbout the Webinar\nThis webinar explores how to harness the full potential of the LUMI supercomputer for large-scale AI model training through efficient utilisation of HPC resources. Participants will learn how thoughtful design of neural network architectures and optimal use of parallelisation techniques—such as model\, data\, and tensor parallelisation—can significantly improve performance and resource efficiency. \nThe session will demonstrate how frameworks like PyTorch and TensorFlow can be leveraged to distribute training workloads effectively across multiple GPUs and nodes on LUMI. Attendees will gain practical insights into balancing computational loads\, minimising communication overhead\, and achieving scalability for advanced AI workloads in an HPC environment. \nWho is the Webinar For\nThis webinar is designed for AI practitioners\, computational scientists\, and HPC users who aim to train large-scale machine learning models efficiently on modern supercomputing infrastructures. It is ideal for professionals seeking to optimise their deep learning workflows by leveraging advanced parallelisation techniques and maximising GPU performance on systems like LUMI. Participants with a background in AI\, data analytics\, or scientific computing who wish to scale their models and improve training efficiency in high-performance environments will particularly benefit from this session. \nKey Takeaways\n\nUnderstand the fundamentals of model\, data\, and tensor parallelisation.\nLearn strategies for efficient AI training on HPC systems like LUMI.\nExplore practical examples using PyTorch and TensorFlow.\nGain insights into optimising GPU utilisation for scalable AI workloads.
URL:https://mimer-ai.eu/event/resource-efficient-ai-model-parallelisation-on-lumi-supercomputer/
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BEGIN:VEVENT
DTSTART;TZID=Europe/Stockholm:20251211T120000
DTEND;TZID=Europe/Stockholm:20251211T133000
DTSTAMP:20260610T071050
CREATED:20251119T184051Z
LAST-MODIFIED:20251210T103000Z
UID:10000002-1765454400-1765459800@mimer-ai.eu
SUMMARY:Profiling AI workloads on GPUs – Identifying performance improvements
DESCRIPTION:| Speakers: \n\nJoakim Stenberg\nRasmus Larsson\nDaniel Gustafsson\nEmelie Wahlström\n\nfrom AMD Silo AI \nAbout the webinar\nThe seminar is for participants 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 seminar. The seminar combines theoretical knowledge with guided coding walkthrough to help participants identify performance improvements\, understand traces\, improve performance\, and enhance efficiency of AI workloads on GPUs. \nWho is the webinar for?\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. \nKey takeaways for participants\n\nUnderstand how to identify and analyze performance improvements for AI workloads on GPUs\nDefine profiling and explain key terminologies\nUnderstand why profiling is important and its connection to performance
URL:https://mimer-ai.eu/event/profiling-ai-workloads-on-gpus-identifying-performance-improvements/
LOCATION:Online
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