BEGIN:VCALENDAR
VERSION:2.0
PRODID:-//Instituto de Astrofísica de Andalucía - ECPv6.17.1//NONSGML v1.0//EN
CALSCALE:GREGORIAN
METHOD:PUBLISH
X-WR-CALNAME:Instituto de Astrofísica de Andalucía
X-ORIGINAL-URL:https://www.iaa.csic.es
X-WR-CALDESC:Eventos para Instituto de Astrofísica de Andalucía
REFRESH-INTERVAL;VALUE=DURATION:PT1H
X-Robots-Tag:noindex
X-PUBLISHED-TTL:PT1H
BEGIN:VTIMEZONE
TZID:Europe/Paris
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
END:VTIMEZONE
BEGIN:VEVENT
DTSTART;VALUE=DATE:20260928
DTEND;VALUE=DATE:20261003
DTSTAMP:20260615T112931Z
CREATED:20260615T112302Z
LAST-MODIFIED:20260615T112931Z
UID:33859-1790553600-1790985599@www.iaa.csic.es
SUMMARY:IAA-CSIC Severo Ochoa School on Artificial Intelligence and Machine Learning in Astronomy
DESCRIPTION:THE SCHOOL\n\n\nOverview & Scientific Rationale\nModern astronomy is entering a data-intensive era in which Artificial Intelligence and Machine Learning are becoming essential scientific tools. From interferometric image reconstruction to source classification and cosmological inference\, AI methods are transforming how astronomical discoveries are made. \nThe Instituto de Astrofísica de Andalucía – Severo Ochoa (IAA-SO) School on Artificial Intelligence and Machine Learning in Astronomy 2026 will provide graduate students\, postdoctoral researchers\, and early-career scientists with practical training in state-of-the-art AI techniques applied to real astronomical datasets from world-leading facilities. \nTraining Modules\nThe school is organized around five major technical pillars\, each led by an expert in the field: \nImage Reconstruction: Optical/infrared interferometry deconvolution utilizing Deep Image Priors Neural Networks (DIP-NN). \nRadio Surveys: Data augmentation and automated morphological classification of radio sources using CNNs and Vision Transformers (ViT). \nLarge-Scale Structure: Graph Neural Networks (GNNs) for classification and regression tasks on dark matter halo simulations. \nGamma-Ray Astronomy: Deep learning-based image and waveform reconstruction for Imaging Atmospheric Cherenkov Telescopes. \nUnsupervised Learning: High-dimensional embeddings (t-SNE\, UMAP\, EVoC) and clustering algorithms applied to chemical tagging of star clusters. \n​​ \nWho should attend?​\n​The school is designed for:​ \n\nMSc and PhD students in astronomy\, physics\, data science\, or related disciplines\nPostdoctoral researchers\nEarly-career scientists interested in AI applications in astronomy\nResearchers seeking practical experience with modern machine learning workflows\n\n\n\n\n \n\n\n			\n				\n				\n				\n				\n				Prerequisites\n\n\n\n\n​Participants are expected to have:​ \n\nBasic Python programming experience.\nFamiliarity with scientific computing tools (NumPy\, Jupyter notebooks).\nBasic astronomy knowledge\n\n  \n​Computing Environment\n\n\n\n\n\n\n\n\nTutorials will be delivered through Python notebooks. Software installation instructions and datasets will be distributed before the school. Participants should bring a laptop capable of running Python scientific workflows. \n\n\n\n\n\n\n\n\n\nLearning outcomes\nParticipants will: \n\nBuild and train modern neural network architectures\nWork directly with astronomical FITS datasets and simulations\nDevelop practical experience with CNNs\, Vision Transformers\, Graph Neural Networks and unsupervised learning methods\nUnderstand strengths and limitations of AI approaches in astronomy\nGain reproducible workflows applicable to their own research\n\n  \n\n\n\n\n			\n				\n				\n				\n				\n				\n\n\n\n\nSpeakers & Lecturers\n\n\nWe are pleased to host an international team of lecturers specializing in different subsets of astronomical machine learning:​​ \n\nDr. Joel Sánchez Bermúdez Instituto de Astronomía (IA-UNAM)\, Mexico\nDr. Andrea DeMarco Institute of Space Sciences and Astronomy (ISSA)\, University of Malta\, Malta\nDr. Farida Farsian Italian National Institute for Astrophysics (INAF)\, Osservatorio Astrofisico di Catania (OACT)\, Italy\nDr. Tjark Miener University of Geneva\, Switzerland / IAA Granada\, Spain\nDr. Rafael Garcia-Dias\, King’s College London\, United Kingdom\n\n\n\n\n​ \n\n\n\n\n			\n				\n				\n				\n				\n				\n\n\n\n\n \n\n\n\n\nSCIENTIFIC ORGANIZING COMMITTEE\n\n\n\nDr. Joel Sánchez Bermúdez (IA-UNAM\, Mexico) — Chair\nDr. Javier Moldón (IAA-CSIC\, Spain) — Co-chair\nDr. Cristóbal Bordiú (IAA-CSIC\, Spain)\nDr. Laura Darriba (IAA-CSIC\, Spain)\nDr. Rubén López-Coto (IAA-CSIC\, Spain)\nDr. Ginés Martínez Solaeche (IAA-CSIC\, Spain)\n\n\n\n \nLOCAL ORGANIZING COMMITTEE\n\n\n\nDr. Laura Darriba (IAA-CSIC)\nDr. Javier Moldón (IAA-CSIC)\nDr. Cristóbal Bordiú (IAA-CSIC)
URL:https://www.iaa.csic.es/evento/iaa-csic-severo-ochoa-school-on-artificial-intelligence-and-machine-learning-in-astronomy/
LOCATION:IAA – CSIC\, Glorieta de la Astronomía\, Granada\, España
CATEGORIES:SO Workshop
ATTACH;FMTTYPE=image/jpeg:https://www.iaa.csic.es/wp-content/uploads/2026/06/Captura-de-pantalla-2026-06-15-a-las-12.49.25.jpg
END:VEVENT
END:VCALENDAR