BEGIN:VCALENDAR VERSION:2.0 PRODID:-//CERN//INDICO//EN BEGIN:VEVENT SUMMARY:Κοπή Πίτας ΕΔΥΤΕ 2026 DTSTART;VALUE=DATE-TIME:20260219T140000Z DTEND;VALUE=DATE-TIME:20260219T190000Z DTSTAMP;VALUE=DATE-TIME:20260311T132500Z UID:indico-event-200@events.grnet.gr DESCRIPTION:\n\n \n\nΗ Διοίκηση του Εθνικού Δικτύ ου Υποδομών\, Τεχνολογίας και Έρευνας (ΕΔ ΥΤΕ Α.Ε.) έχει τη χαρά να σας προσκαλέσει στην εκδήλωση για την Κοπή της Βασιλόπιτ ας του 2026.\n\n📅 Πέμπτη 19 Φεβρουαρίου 2026\n 🕔 Ώρα: 16:00 – 21:00\n📍 Χώρος: 48 Urban Garden\n\nΗ συ νάντηση αυτή αποτελεί μια ξεχωριστή ευκ αιρία να κάνουμε έναν απολογισμό της χρο νιάς που πέρασε\, να μοιραστούμε τους στό χους μας για το νέο έτος και να περάσουμε όμορφες στιγμές μαζί.\n\nΣημειώνεται ότι\ , λόγω περιορισμένης χωρητικότητας\, η πρ όσκληση απευθύνεται αποκλειστικά στον/σ την παραλήπτη/παραλήπτρια και δεν ισχύε ι για συνοδούς.\n\nΠαρακαλούμε όπως επιβε βαιώσετε τη συμμετοχή σας μέσω του σχετι κού συνδέσμου.\n\nΘα χαρούμε ιδιαίτερα να σας δούμε από κοντά!\n\nΥ.Γ. Γ. Έχουμε δημιο υργήσει φάκελο όπου μπορείτε να ανεβάζε τε τις δικές σας φωτογραφίες σας. Στον ιδ ιο φάκελο\, θα βρείτε λίγες μέρες μετά τη ν εκδήλωση και τις επαγγελματικές φωτογ ραφίες.\n\n \nhttps://events.grnet.gr/event/200/ LOCATION: URL:https://events.grnet.gr/event/200/ END:VEVENT BEGIN:VEVENT SUMMARY:HPC Training Series - Course 20 "Gradient-based and gradient-free optimization\, with applications to CFD and beyond" DTSTART;VALUE=DATE-TIME:20260304T080000Z DTEND;VALUE=DATE-TIME:20260304T130000Z DTSTAMP;VALUE=DATE-TIME:20260311T132500Z UID:indico-event-207@events.grnet.gr DESCRIPTION:\n\nEuroCC@Greece and the National Technical University of At hens announce the 20th Course of HPC Training Series with the subject "G radient-based and gradient-free optimization\, with applications to CFD an d beyond". \n\nDate: March 4th\, 2026\, at 10:00 EET  \n\nLocation: On line via Zoom  \n\nPresentation Language: Greek\n\nAudience: \n\nSuitabl e for students\, researchers\, and engineers interested in understanding a nd using optimization algorithms in CFD and other disciplines. \n\nCourse Description: \n\nThis course includes an introduction to the adjoint-assis ted\, gradient-based optimization workflow of the open-source CFD toolbox\ , OpenFOAM\, with some demo applications and continues with an introductio n to gradient-free optimization methods\, such as evolutionary algorithms\ , with emphasis on metamodels and artificial intelligence for reducing the optimization turnaround time. \n\nLearning Objectives:\n\nThe objective o f the training is to introduce newcomers to basic concepts related to grad ient-based and gradient-free optimization\, in the field of Computational Fluid Dynamics (CFD) and beyond. At the end of the training\, the attendee s should have understood what shape and topology optimization is and shoul d be able to set up a simple shape optimization case in OpenFOAM. Addition ally\, attendees should have understood the pros and cons of gradient-base d and gradient-free optimization methods and be able to choose between the m\, depending on their optimization problem.\n\nPrerequisites: \n\nA prio r knowledge of the basics of OpenFOAM is beneficial for following the corr esponding part of the course. Basic understanding of fluid mechanics\, lin ear algebra and CFD are also useful background knowledge for following the training.\n\n \n\n \n\nNote: Please enter your institutional/corporate email when registering.\n\n \nhttps://events.grnet.gr/event/207/ LOCATION: URL:https://events.grnet.gr/event/207/ END:VEVENT BEGIN:VEVENT SUMMARY:PHAROS Training Series - Course 4 "Introduction to Time Series For ecasting" DTSTART;VALUE=DATE-TIME:20260309T093000Z DTEND;VALUE=DATE-TIME:20260309T141500Z DTSTAMP;VALUE=DATE-TIME:20260311T132500Z UID:indico-event-202@events.grnet.gr DESCRIPTION:\n\nPHAROS AI Factory announces the 4th Course of its Training Series\, under the title "Introduction to Time Series Forecasting"\, Trac k "Machine Learning"\, held online via Zoom.  \n\nDate: March 9th\, 2026 \, at 11:30 EET \n\nLocation: Online via Zoom\n\nPresentation Language: Greek\n\nAudience: This course is intended for postgraduate students in co mputer science and economics\, researchers\, employees of small and medium -sized enterprises (SMEs)\, data scientists\, and machine learning enginee rs\, as well as anyone interested in applying time series forecasting tech niques to real-world\, data-driven problems.\n\nCourse Description:\nThis course provides a complete\, hands-on introduction to time series forecast ing\, starting from the fundamentals of what time series data is and why i ts analysis is essential. Participants will learn why forecasting plays a critical role in decision making\, how data analysis and preprocessing are integrated into the forecasting pipeline\, and how insights are extracted from temporal data. The session covers classical statistical models for t ime series forecasting. Through practical\, end-to-end examples\, attendee s will implement forecasting models\, gain a solid understanding of the un derlying theory\, and apply these techniques to real-world scenarios.\n\nL earning Objectives:\n\nThis course provides a complete\, hands-on introduc tion to time series forecasting\, starting from the fundamentals of what t ime series data is and why its analysis is essential. Participants will le arn why forecasting plays a critical role in decision making\, how data an alysis and preprocessing are integrated into the forecasting pipeline\, an d how insights are extracted from temporal data. The session covers both c lassical statistical models and modern machine learning and deep learning approaches for time series forecasting. Through practical\, end-to-end exa mples\, attendees will implement forecasting models\, understand the theor y behind them\, and apply these techniques to real-world scenarios.\n\nLea rning Outcomes: Solve a specific problem\n\nUpon successful completion of this course\, participants will be able to systematically handle time seri es data\, applying appropriate analytical tools and methodologies to addre ss real-world forecasting challenges. They will develop a structured analy tical mindset\, understanding the end-to-end forecasting pipeline—from p roblem definition and data preprocessing to model selection\, evaluation\, and interpretation of results—while being able to identify patterns\, t rends\, and seasonality within temporal data.\n\nInstructor's profile:\nEf terpi Paraskevoulakou is a Research Assistant and PhD Candidate at the Uni versity of Piraeus\, specializing in AI-driven orchestration and optimizat ion of Cloud–Edge Continuum applications. Her research focuses on intell igent resource allocation\, infrastructure optimization\, and time-series –based predictive analytics for large-scale distributed environments. He r publications include studies on Dynamic Resource Allocation on the Edge and Machine Learning Functions as a Service\, contributing to intelligent orchestration frameworks that integrate AI\, optimization\, and time-serie s forecasting for next-generation computing continuum environments.\n\nPre requisites: Participants should have intermediate-level knowledge of the P ython programming language and a basic understanding of data analysis conc epts. \n\nNote: Please enter your institutional/corporate email when regis tering.\n\n \nhttps://events.grnet.gr/event/202/ LOCATION: URL:https://events.grnet.gr/event/202/ END:VEVENT BEGIN:VEVENT SUMMARY:PHAROS Training Series - Course 5 "Trustworthy and Explainable AI in Health" DTSTART;VALUE=DATE-TIME:20260313T100000Z DTEND;VALUE=DATE-TIME:20260313T144500Z DTSTAMP;VALUE=DATE-TIME:20260311T132500Z UID:indico-event-203@events.grnet.gr DESCRIPTION:\n\nPHAROS AI Factory announces the 5th Course of its Training Series\, under the title "Trustworthy and Explainable AI in Health"\, Tra ck  AI4Health\, held online via Zoom.  \n\nDate: March 13th\, 2025\, a t 12:00 EET \n\nLocation: Online via Zoom\n\nPresentation Language: Gree k\n\nPART I:  FUTURE-AI principles for Trustworthy AI \n\nSpecialisation: AI4Health \n\nModule Description: The rapid adoption of Artificial Intell igence in critical sectors such as healthcare\, industry\, and public admi nistration makes the development of trustworthy\, transparent\, and human- centric AI systems essential. The FUTURE-AI framework provides a comprehen sive set of principles and practices for building Trustworthy AI throughou t the entire AI lifecycle. This webinar offers an in-depth introduction to the FUTURE-AI Principles\, covering six core dimensions: Fairness\, Unive rsality\, Traceability\, Usability\, Robustness\, and Explainability. Part icipants will explore how these principles translate into concrete technic al\, organizational\, and governance practices\, aligned with the EU AI Ac t and international AI standards. Through an expert-led presentation\, rea l-world examples\, and interactive case studies (with a strong focus on he althcare applications)\, the session bridges theory and practice. Particul ar emphasis is placed on trustworthiness by design\, risk assessment\, and compliance strategies for high-risk AI systems. The webinar is designed f or professionals\, researchers\, and decision-makers who aim to design\, e valuate\, deploy\, or procure AI systems responsibly\, ensuring societal a cceptance\, regulatory compliance\, and long-term sustainability. \n\nAudi ence: \n\nSuitable for:\n\n\n AI and Machine Learning researchers and engi neers\n Data Scientists and AI Architects\n Healthcare professionals using AI systems\n Innovation managers and digital transformation leaders\n Pol icy makers\, ethics officers\, and compliance experts\n Graduate and PhD s tudents\n\n\nLearning Objectives:\n\nBy participating\, attendees will:\n\ n\n Gain a deep understanding of the FUTURE-AI principles\n Understand the relationship between Trustworthy AI and the EU AI Act\n Engage directly w ith experts on practical implementation challenges\n Connect theoretical p rinciples to real-world AI use cases\n Apply FUTURE-AI concepts in profess ional or project-based contexts\n\n\nLearning Outcomes: \n\nAfter complet ion\, participants will be able to:\n\n\n Explain the core principles of t he FUTURE-AI framework\n Analyze risks and trust gaps in AI systems\n Asse ss whether an AI system meets Trustworthy AI requirements\n Identify compl iance and governance steps for AI deployment\n Define next steps for furth er learning or implementation\n\n\nInstructor's profile:\nHaridimos Kondyl akis is an Associate Professor of Big Data Engineering at the University o f Crete and a collaborating researcher at the Institute of Computer Scienc e\, FORTH. His research focuses on big data management\, semantic technolo gies\, artificial intelligence\, and AI applications in healthcare and oth er high-risk domains. He has extensive experience in EU-funded research pr ojects and has published widely in leading international journals and conf erences. His work emphasizes the responsible\, trustworthy\, and interoper able use of AI and he is one of the co-authors of the FUTURE-AI principles .\n\nPART II:  From Quantitative MRI to Explainable AI models: Applicatio ns in Cancer Imagin\n\nSpecialisation: AI4Health \n\nModule Description:\n \nExplainable AI models play a critical role in healthcare\, particularly in medical imaging\, where clinical decisions must be transparent\, trustw orthy\, and accountable. Unlike black-box models (mainly deep learning met hods)\, explainable approaches provide insights into how predictions are m ade\, In medical imaging tasks such as tumor detection\, disease classific ation\, and image segmentation\,explainable AI techniques help clinicians verify whether an AI system is focusing on medically relevant structures r ather than misleading patterns. This interpretability not only increases c linician confidence and supports clinical decision-making\, but also aids in model validation\, bias detection\, and generalization. In practice\, m odel explainability is presented by highlighting image regions that most i nfluenced a diagnosis or by quantifying the contribution of specific featu res. In conclusion\, explainable AI bridges the gap between high-performin g AI models and the need for safety\, reliability\, and generalizability i n real-world healthcare settings.\n\nAudience: \n\nSuitable for:\n\n\n  A I and Machine Learning researchers and engineers\n  Data Scientists \n  Healthcare professionals using AI systems\n  Clinicians/Radiologists or medical techologists with enthusiasm in AI \n  Graduate and PhD student s\n\n\nLearning Objectives:\n\nBy participating\, attendees will understan d:\n\n\n  The principles of cancer imaging\n  Basic Principles of MRI mo deling and quantification\n  Radi-(omics) based ML models / Fusion with q uantitative MRI\n  Explainability of radiology-based features\n\n\nLearni ng Outcomes: \n\nAfter completion\, participants will be able to:\n\n\n  Explain the principles of the cancer Imaging\n  Identify MRI sequence/ protocol \n  Train a Radiomic based ML model\n  Explain radiological mo del outcomes\n\n\nInstructor's profile:\n\nGeorge Ioannidis is a mathemati cian with a master's degree in Applied and Computational Mathematics from the Department of Mathematics and Applied Mathematics at the University of Crete. In 2020\, he received his Ph.D. from the Medical School of the Uni versity of Crete in the radiology/medical physics domain. Currently\, he w orks as a postdoctoral researcher at the Computational Bio-Medicine Labora tory of the Foundation for Research and Technology - Hellas (FORTH). Excep t from the research activity he also works as an adjunct lecturer at the d epartment of Biomedical Sciences at University of west Attica.Furthermore\ , he was involved in various projects (Greek and European) such as (APOSID I\, Pro-CancerI\, Radioval\, Cardio-Care) as a medical image-analysis/Mach ine learning expert. His main interests include the modeling of biological processes from magnetic resonance and computed tomography imaging data fo r biomarker extraction\, medical image and signal processing\, and the dev elopment of numerical methods and machine learning algorithms for the crea tion of clinical applications.\n \n\nNote: Please enter your institutiona l/corporate email when registering.\n\n \nhttps://events.grnet.gr/event/2 03/ LOCATION: URL:https://events.grnet.gr/event/203/ END:VEVENT BEGIN:VEVENT SUMMARY:PHAROS Training Series - Course 6 "Vision Representation Learning and Generative Models in Biomedicine" DTSTART;VALUE=DATE-TIME:20260316T100000Z DTEND;VALUE=DATE-TIME:20260316T144500Z DTSTAMP;VALUE=DATE-TIME:20260311T132500Z UID:indico-event-204@events.grnet.gr DESCRIPTION:\n\nPHAROS AI Factory announces the 6th Course of its Training Series\, under the title "Vision Representation Learning and Generative M odels in Biomedicine"\, under the Specialisation AI4Health.\n\nDate: March 16th\, 2026\, at 12:00 EET \n\nLocation: Online via Zoom\n\nPresentatio n Language: Greek\n\nDescription: The course focuses on state-of-the-art m achine learning techniques for extracting and modeling information from bi omedical images. The course introduces how deep neural networks learn visu al representations that enable robust performance in tasks such as classif ication\, segmentation\, and anomaly detection in medical and biological i maging.\nThe first part of the webinar covers vision representation learni ng\, emphasizing convolutional neural networks\, transfer learning\, and s elf-supervised and contrastive learning methods. Participants will learn h ow representations can be learned from large-scale unlabeled biomedical da tasets and adapted to downstream tasks where labeled data is scarce. Real- world examples from histopathology and medical imaging will be discussed t o illustrate best practices and common challenges.\nThe second part of the webinar introduces generative models\, including Variational Autoencoders (VAEs) and Generative Adversarial Networks (GANs). These models are exami ned in the context of biomedical applications such as data augmentation\, image synthesis\, latent space interpretation\, and hypothesis generation. The session highlights how generative modeling can support both predictiv e performance and scientific discovery by capturing the underlying structu re of complex biological data.\nThe webinar combines theory\, visual demon strations\, and interactive discussions to provide participants with a pra ctical understanding of how representation learning and generative models are applied in modern biomedical AI pipelines.\n\nAudience: \n\n\n Data Sc ientists and Machine Learning Engineers\n Biomedical and Computational Res earchers\n PhD and MSc students in AI\, Biomedical Engineering\, or Comput er Vision\n Professionals in medical technology\, digital health\, and lif e sciences\n\n\nLearning Objectives:\n\nBy the end of the course\, partici pants will:\n\n\n Gain a deeper understanding of vision representation lea rning techniques in biomedicine\n Understand the role of generative models in biomedical image analysis\n Learn how modern deep learning architectur es are applied to medical and biological data\n Engage with practical exam ples and case studies drawn from real-world research\n\n\nLearning Outcome s: \n\nAfter completing the course\, participants will be able to:\n\n\n Explain core principles of representation learning and generative modeling \n Analyze biomedical imaging problems and select appropriate modeling app roaches\n Evaluate when and how generative models can be applied in biomed ical projects\n Identify next steps for further research or implementation in professional settings\n\n\nInstructors' profiles:\nGrigorios Kalliatak is is a collaborating researcher at Computational BioMedicine Laboratory (CBML) of Institute of Computer Science\, Foundation for Research and Tech nology - Hellas (FORTH). His research interests are in the areas of comput er vision\, deep learning\, unsupervised learning\, and representation lea rning\, focusing on applications to healthcare. He has participated in lar ge-scale UK research projects (ESRC and Cancer Research UK) as well as EU- funded projects. He has authored more than 20 publications in high profile journals and conferences (Radiology\, CVPR\, ICCV).\n\nPrerequisites: Bas ic understanding of Deep Learning. \n\nNote: Please enter your institution al/corporate email when registering.\n\n \nhttps://events.grnet.gr/event/ 204/ LOCATION: URL:https://events.grnet.gr/event/204/ END:VEVENT BEGIN:VEVENT SUMMARY:HPC Training Series - Course 21 "HPC for Beginners: SLURM\, MPI an d OpenMP" DTSTART;VALUE=DATE-TIME:20260320T074500Z DTEND;VALUE=DATE-TIME:20260320T124500Z DTSTAMP;VALUE=DATE-TIME:20260311T132500Z UID:indico-event-206@events.grnet.gr DESCRIPTION:\n\nEuroCC@Greece announces the 21st Course of HPC Training Series with the subject "HPC for Beginners: SLURM\, MPI and OpenMP".\n\n Date: March 20th\, 2026\, at 09:45 EET  \n\nLocation: Online via Zoom   \n\nPresentation Language: Greek\n\nAudience: \n\nSuitable for all users \, with a focus on:\n\n\n \n Engineers\n \n \n Developers\n \n \n IT Profe ssionals\n \n \n Students\n \n \n Researchers\n \n\n\nThis course is ideal for those looking to understand the basics of how parallel computing tech nologies\, such as MPI and OpenMP\, can improve code performance.\n\nCours e Description: \n\nThis is an introductory course designed for engineers\, developers\, IT professionals\, students\, and researchers looking to enh ance code performance using parallel computing. Participants will learn fu ndamental concepts of High-Performance Computing (HPC)\, with a focus on s hared and distributed memory parallelism. The course covers the open-sourc e workload manager and job scheduler SLURM (Simple Linux Utility for Resou rce Management)\, OpenMP for shared memory parallelism\, and MPI for distr ibuted memory computing\, introducing both single-node and multi-node para llel techniques\, respectively. Additionally\, participants will gain insi ghts into accessing Greek and European supercomputers for research and dev elopment. No prior HPC experience is required\, but basic programming know ledge (C\, C++\, or Fortran) is recommended.  \n\nLearning Objectives:\n\ nBy the end of this course\, participants will be able to:\n\n\n \n Unders tand the fundamental concepts of High-Performance Computing (HPC)\n \n \n Explain the basic principles of parallel computing and how it enhances cod e performance.\n \n \n Use Slurm to schedule jobs on an HPC cluster.\n \n \n Apply OpenMP for shared memory parallelism using basic constructs.\n \n \n\n\n \n Utilize MPI for distributed memory parallelism under blocking an d non-blocking communication.\n \n\n\nPrerequisites: \n\nParticipants sho uld have:\n\n\n \n Basic programming knowledge (preferably in C\, C++\, or Fortran).\n \n \n A general understanding of computer science concepts.\n \n \n No prior experience with HPC\, MPI\, or OpenMP is required.\n \n\n\ n \n\nNote: Please enter your institutional/corporate email when register ing.\n\n \nhttps://events.grnet.gr/event/206/ LOCATION: URL:https://events.grnet.gr/event/206/ END:VEVENT BEGIN:VEVENT SUMMARY:PHAROS Training Series - Course 7 "Quantitative Pathologic Assessm ent using AI-based Whole-Slide Image Analysis" DTSTART;VALUE=DATE-TIME:20260320T100000Z DTEND;VALUE=DATE-TIME:20260320T144500Z DTSTAMP;VALUE=DATE-TIME:20260311T132500Z UID:indico-event-205@events.grnet.gr DESCRIPTION:\n\nPHAROS AI Factory announces the 7th Course of its Training Series\, under the title "Quantitative Pathologic Assessment using AI-bas ed Whole-Slide Image Analysis"\, under the specialisation AI4Health\, held online via Zoom.  \n\nDate: March 20th\, 2026\, at 12:00 EET \n\nLocat ion: Online via Zoom\n\nPresentation Language: Greek\n\nDescription: Dig ital pathology has revolutionized the field of histopathological analysis\ , enabling unprecedented opportunities for quantitative assessment of tiss ue specimens. This course introduces a comprehensive AI-driven infrastruct ure designed to transform conventional pathology workflows into precise\, reproducible\, and scalable analytical pipelines. We will explore cutting- edge deep learning methodologies for automated tissue segmentation\, enabl ing accurate delineation of tumor regions\, stroma\, necrosis\, and other morphologically distinct areas within whole-slide images (WSIs). Building upon this foundation\, we present advanced cell classification algorithms capable of identifying and categorizing diverse cellular populations with high accuracy and throughput. A significant focus will be placed on quanti tative feature extraction from both traditional Hematoxylin and Eosin (H&E ) stained sections and multiplex immunohistochemistry (mIHC) images. These computational approaches enable the derivation of morphometric\, spatial\ , and contextual features that capture the complex tumor microenvironment architecture. Furthermore\, we will demonstrate practical applications of deployed AI models for clinically relevant predictions\, including molecul ar mutation status inference directly from histopathology images\, patient survival outcome stratification\, and treatment response prediction throu gh pathologic complete response (pCR) assessment. These predictive models leverage the extracted quantitative features to provide actionable insight s that can guide therapeutic decision-making. Throughout the presentation\ , emphasis will be placed on infrastructure design\, model validation stra tegies\, and clinical integration considerations essential for translating AI-based pathology tools from research settings to routine diagnostic pra ctice.\n\nAudience: \n\nThis course is suitable for:\n\n\n Pathologists an d histopathology professionals\n Computational biologists and data scienti sts\n Oncology researchers and clinicians\n Medical imaging specialists\n Pharmaceutical/biotech R&D professionals\n\n\nLearning Objectives:\n\nBy p articipating in this webinar\, attendees will:\n\n\n Understand the princi ples of AI-based whole-slide image analysis and the infrastructure for dig ital pathology workflows.\n Describe deep learning approaches for automate d tissue segmentation and cell classification in histopathology.\n Identif y quantitative features extractable from H&E and multiplex immunohistochem istry images to characterize tumor microenvironment.\n Evaluate predictive AI models for mutation inference\, survival stratification\, and treatmen t response prediction.\n Recognize key considerations for validating and d eploying AI-based pathology tools into clinical practice.\n\n\nLearning Ou tcomes:\n\nAfter completing the course\, participants will be able to unde rstand:\n\n\n AI infrastructure for whole-slide image analysis\n Tissue se gmentation and cell classification AI methods\n Quantitative feature extra ction from H&E and mIHC images using AI and image analysis techniques\n Ho w predictive models work for mutations\, survival\, and pCR prediction\n A I deployment strategies in clinical pathology\n\n\nInstructor's profile:\n Georgios Manikis is a Marie Skłodowska-Curie Postdoctoral Fellow at the U niversity of Cyprus and his fellowship research is dedicated to AI-driven computational pathology and multimodal predictive modeling in oncology. He is a collaborating researcher at the Computational BioMedicine Laboratory (CBML)\, FORTH and the Karolinska Institutet\, Departent of Oncology-Path ology. His research interests lie in the areas of medical image analysis\, machine and deep learning analysis.\n\nNote: Please enter your institutio nal/corporate email when registering.\n\n \nhttps://events.grnet.gr/event /205/ LOCATION: URL:https://events.grnet.gr/event/205/ END:VEVENT END:VCALENDAR