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DTSTART;TZID=Europe/Stockholm:20260616T110000
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DTSTAMP:20260613T142523
CREATED:20260507T101956Z
LAST-MODIFIED:20260513T082057Z
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SUMMARY:Trustworthy AI in practice
DESCRIPTION:About the webinar\nThe webinar will begin with a short introduction to the EU AI Act and the core principles of Trustworthy AI. The team will present the Mimer Trustworthy AI Self‑Assessment Tool (the SATisfiability)\, developed to support organizations in navigating these requirements. The tool is based on the ALTAI questionnaire and the EU AI Act\, with further development guided by forthcoming CEN‑CENELEC standards. It provides users with a clear overview of how trustworthy their AI system or concept is\, while highlighting the key regulatory and ethical requirements they should prioritize. Using this tool gets the users closer to understand their own developments in relation to trustworthiness as well as the EU AI Act. \nWho is the webinar for?\nSMEs\, Tech personnel interested in Trustworthy AI\, Leaders in tech industry interested to understand Trustworthy AI requirements. \nKey takeaways for participants\n\nAn introductory understanding of Trustworthy AI\nHow to use the Mimer developed Trustworthy AI Self Assessment Tool\n\nSpeaker bio\n\nFahria Kabir is an R&D Engineer at RISE\, focusing on trustworthy AI and space technology. Kabir currently serves as a Tech Lead in the Trustworthy AI Team at the Swedish AI Factory Mimer. Their interests include trustworthy AI\, the EU AI Act\, human oversight\, transparency\, explainable AI (XAI)\, and intrusion detection systems.\nSusanne Stenberg is Policy Expert in Trustworthy AI and Autonomous Systems at MIMER. Susanne works with applied legal research\, including in standardizations and operating policy labs. Her expertise is technology development in harmony with regulation\, regulatory sandboxes and testing activities. Senior researcher at RISE and the RISE center of Applied AI.
URL:https://mimer-ai.eu/event/trustworthy-ai-in-practice/
LOCATION:Online
ATTACH;FMTTYPE=image/webp:https://mimer-ai.eu/wp-content/uploads/2026/05/trustworthy-webinar.webp
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DTSTART;TZID=Europe/Stockholm:20260617T140000
DTEND;TZID=Europe/Stockholm:20260617T150000
DTSTAMP:20260613T142523
CREATED:20260526T081238Z
LAST-MODIFIED:20260527T081830Z
UID:10000022-1781704800-1781708400@mimer-ai.eu
SUMMARY:Crash course in AI ethics
DESCRIPTION:About the webinar\nMinimal dose of Ethics of AI for all AI Factory use cases. \nWho is the webinar for?\nThe Crash Course in AI Ethics is universal and aimed at anyone who develops\, deploys\, or uses AI. \nKey takeaways for participants:\n\n\nGetting a sense of what is the role of ethics in technology. \n\n\nBeing able to identify the most common applied issues in AI ethics. \n\n\nKnowing next steps where to get support at MIMER regarding ethics\, if needed. \n\n\nSpeaker bio:\nLaurynas Adomaitis is an AI Ethics and Governance researcher at RISE Research Institutes of Sweden. Laurynas is now part of the Computer Science Department. He has previously worked for a software company as an Innovation Manager\, and as a researcher at the French Atomic Energy Commission. He has 7 years of experience in AI Ethics\, is chairing the international working group on Ethics for all AI Factories and Antennas\, and is a Co-Chair of the Digital Ethics working group at the European Research Consortium for Informatics and Mathematics.
URL:https://mimer-ai.eu/event/crash-course-in-ai-ethics/
LOCATION:Online
ATTACH;FMTTYPE=image/webp:https://mimer-ai.eu/wp-content/uploads/2026/05/ai-ethics-webinar.webp
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BEGIN:VEVENT
DTSTART;TZID=Europe/Stockholm:20260922T090000
DTEND;TZID=Europe/Stockholm:20260924T120000
DTSTAMP:20260613T142523
CREATED:20260612T071555Z
LAST-MODIFIED:20260612T071555Z
UID:10000023-1790067600-1790251200@mimer-ai.eu
SUMMARY:DEEP Inspection for Materials Science - Classification
DESCRIPTION:Register latest by: 11 September 2026\nAbout the event\nInspection and characterization of materials are fundamental for understanding material properties\, ensuring quality control\, and accelerating materials development and advanced manufacturing. However\, conventional approaches often require extensive expert analysis of complex images\, making them time-consuming and difficult to scale. With the rapid growth of high-resolution imaging techniques\, deep learning-based methods provide powerful solutions for automated materials image analysis by learning meaningful visual features for tasks such as defect classification\, microstructure recognition\, process monitoring\, and intelligent quality assessment. \nThis workshop offers a focused\, hands-on introduction to deep learning for visual inspection in materials science. Using the Severstal Steel Defect Detection dataset as a concrete\, running example\, participants will follow a complete analysis pipeline\, from raw image data through model training to interpretable results. The techniques covered are deliberately chosen for their breadth of applicability: while the examples are grounded in industrial quality control\, the same approaches translate directly to defect detection in electron microscopy\, anomaly identification in medical imaging\, and structural characterization across a wide range of domains. \nOver three half-day sessions\, participants will build solid intuition for how modern neural networks process and learn from image data\, implement deep neural network classifiers from scratch\, apply transfer learning to leverage pre-trained models for materials-specific tasks\, and explore state-of-the-art architectures (including convolutional networks and transformer-based approaches) used across both research and industry. \nWe encourage participants to follow up in our upcoming workshop session on Sept. 30th to dive deeper into object detection and instance segmentation. \nWho is this course for?\n\n\nEngineers\, analysts\, academic researchers\, and students in materials science or manufacturing who want to apply AI-based image analysis to quality control and defect inspection \n\n\nAnyone with a general interest in computer vision and deep learning\, regardless of domain \n\n\nKey takeaways for participants\n\n\nUnderstand the core concepts behind deep neural networks (including CNNs) and transfer learning and know when and why to use them for image-based inspection tasks \n\n\nGain hands-on experience building and training defect classifiers — from a simple binary classifier to multi-class CNNs \n\n\nLearn how to extract and visualize feature embeddings to understand what your model has learned \n\n\nKnow how to leverage pretrained models (VGG\, ResNet\, ViT) through transfer learning\, even with limited amount of data \n\n\nPrerequisites\n\n\nBasic Python programming (loops\, functions\, libraries) \n\n\nFamiliarity with NumPy or similar data manipulation tools is helpful but not required \n\n\nNo prior deep learning experience necessary – core concepts will be introduced from scratch \n\n\nSchedule\n\n\n\nDay\nTime\nContents\n\n\n\n\n22 September 2026\n09:00 – 12:00\nAnomaly Detection & Feature Extraction\nIntroduction to industrial inspection and defect classification. Build a binary defect classifier\, explore learned embeddings\, and visualize feature clusters\n\n\n23 September 2026\n09:00 – 12:00\nCNN Fundamentals & Multi-class Classification\nDive into convolutional neural networks — convolutions\, pooling\, and the LeNet architecture. Apply training best practices\n\n\n24 September 2026\n09:00 – 12:00\nTransfer Learning & Advanced Vision Architectures Explore pretrained deep networks (VGG\, ResNet) and Vision Transformers (ViT). Fine-tune models on defect data and compare performance across architectures.\n\n\n\n\nInstructors and Teaching Assistants\n\n\nBenedikt Neyses (Mimer AIF / RISE) \n\n\nAndreas Thore (Mimer AIF / RISE) \n\n\nMarzieh Saeedimasine (MIMER AIF/NAISS) \n\n\nRuiwen Xie (MIMER AIF/NAISS) \n\n\nSmita Chakraborty (Mimer AIF / RISE) \n\n\nYuvarajendra Anjaneya Reddy (Mimer AIF / RISE) \n\n\nYonglei Wang (MIMER AIF/NAISS)
URL:https://mimer-ai.eu/event/deep-inspection-for-materials-science-classification/
ATTACH;FMTTYPE=image/webp:https://mimer-ai.eu/wp-content/uploads/2026/05/Mimer-workshop.webp
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BEGIN:VEVENT
DTSTART;TZID=Europe/Stockholm:20261001T090000
DTEND;TZID=Europe/Stockholm:20261001T153000
DTSTAMP:20260613T142523
CREATED:20260612T071539Z
LAST-MODIFIED:20260612T071539Z
UID:10000024-1790845200-1790868600@mimer-ai.eu
SUMMARY:DEEP Inspection for Materials Science - Detection and Segmentation
DESCRIPTION:Register latest by: 18 September 2026\nAbout the event\nDetecting where a defect is — not just whether it exists — is the next step in developing deep learning model for real-world materials inspection. Localization and delineation of features within an image unlocks a far richer level of analysis\, whether the subject is a steel surface\, a biological tissue sample\, or a microscopy image of a crystalline material. Moving from image-level classification to precise localization and pixel-level segmentation enables a more detailed understanding of material surfaces\, supporting applications such as quality control\, failure analysis\, and process optimization in advanced manufacturing. \nThis one-day workshop focuses on object detection and instance segmentation\, equipping participants with the tools to localize and delineate multiple defects within a single image. Steel defect detection and glass fiber analysis serve as hands-on case studies\, which will provide concrete and well-annotated datasets to work with\, but the techniques and workflow are directly transferable to a wide range of scientific and industrial imaging contexts. By the end of the session\, participants will have progressed from raw annotated data to a trained\, inference-ready YOLO model within a single session. \nWho is this for?\n\n\nEngineers\, analysts\, and domain specialists who need to move beyond pass/fail classification toward precise spatial localization of features within images \n\n\nResearchers and students with basic familiarity with deep learning who want to extend their skills into detection and segmentation workflows \n\n\nKey takeaways for participants\n\n\nUnderstand the difference between classification\, object detection\, and segmentation — and when each is appropriate for inspection tasks \n\n\nLearn how YOLO works end-to-end: from bounding box prediction and confidence scoring to non-maximum suppression and real-time inference \n\n\nGain hands-on experience training YOLO from scratch and fine-tuning a pretrained YOLO model on real-world datasets. \n\n\nKnow how to prepare annotated data\, configure a training pipeline\, and evaluate and visualize model outputs \n\n\nPrerequisites\n\n\nBasic Python programming \n\n\nFamiliarity with deep learning concepts (neural networks\, training loops) — ideally from Week 1 of this workshop series or equivalent experience \n\n\nNo prior experience with object detection required \n\n\nSchedule\n\n\n\nDay\nTime\nContents\n\n\n\n\n1 October 2026\n09:00 – 12:00\nIntroduction to detection & segmentation\, detection fundamentals (bounding boxes\, IoU\, NMS)\, YOLO architecture part 1 (object detection)\, hands-on: dataset preparation & configuration\n\n\n1 October 2026\n13:00-15:30\nYOLO architecture part 2 (segmentation)\, transfer learning\, hands-on: load pretrained weights\, train YOLO\, evaluation\, inference & visualization\, buffer & wrap-up\n\n\n\n\nInstructors and Teaching Assistants\n\n\nAndreas Thore (Mimer AIF / RISE) \n\n\nSmita Chakraborty (Mimer AIF / RISE) \n\n\nMarzieh Saeedimasine (MIMER AIF/NAISS) \n\n\nRuiwen Xie (MIMER AIF/NAISS) \n\n\nYuvarajendra Anjaneya Reddy (Mimer AIF / RISE) \n\n\nYonglei Wang (MIMER AIF/NAISS)
URL:https://mimer-ai.eu/event/deep-inspection-for-materials-science-detection-segmentation/
ATTACH;FMTTYPE=image/webp:https://mimer-ai.eu/wp-content/uploads/2026/05/Mimer-workshop.webp
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