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DTSTART;TZID=Europe/Stockholm:20260421T140000
DTEND;TZID=Europe/Stockholm:20260421T153000
DTSTAMP:20260419T185548
CREATED:20260312T142620Z
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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
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BEGIN:VEVENT
DTSTART;TZID=Europe/Stockholm:20260423T090000
DTEND;TZID=Europe/Stockholm:20260423T120000
DTSTAMP:20260419T185548
CREATED:20260413T114338Z
LAST-MODIFIED:20260413T114907Z
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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
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BEGIN:VEVENT
DTSTART;TZID=Europe/Stockholm:20260429T090000
DTEND;TZID=Europe/Stockholm:20260429T150000
DTSTAMP:20260419T185548
CREATED:20260305T141654Z
LAST-MODIFIED:20260313T141535Z
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SUMMARY:Hands-on Kubernetes as a User
DESCRIPTION:Register latest by: 24 April 2026\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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BEGIN:VEVENT
DTSTART;TZID=Europe/Stockholm:20260527T100000
DTEND;TZID=Europe/Stockholm:20260527T113000
DTSTAMP:20260419T185548
CREATED:20260316T103842Z
LAST-MODIFIED:20260316T120826Z
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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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