{"version":"1.0","provider_name":"ISAC","provider_url":"https:\/\/www.isac.cnr.it\/en\/","author_name":"marcomaria.grande","author_url":"https:\/\/www.isac.cnr.it\/en\/author\/marcomaria-grande\/","title":"MACHINE LEARNING TECHNIQUES FOR ATMOSPHERIC AND CLIMATE SCIENCES - ISAC","type":"rich","width":600,"height":338,"html":"<blockquote class=\"wp-embedded-content\" data-secret=\"91RzADrOuh\"><a href=\"https:\/\/www.isac.cnr.it\/en\/group\/ml-wg\/\">MACHINE LEARNING TECHNIQUES FOR ATMOSPHERIC AND CLIMATE SCIENCES<\/a><\/blockquote><iframe sandbox=\"allow-scripts\" security=\"restricted\" src=\"https:\/\/www.isac.cnr.it\/en\/group\/ml-wg\/embed\/#?secret=91RzADrOuh\" width=\"600\" height=\"338\" title=\"&#8220;MACHINE LEARNING TECHNIQUES FOR ATMOSPHERIC AND CLIMATE SCIENCES&#8221; &#8212; ISAC\" data-secret=\"91RzADrOuh\" frameborder=\"0\" marginwidth=\"0\" marginheight=\"0\" scrolling=\"no\" class=\"wp-embedded-content\"><\/iframe><script>\n\/*! This file is auto-generated *\/\n!function(d,l){\"use strict\";l.querySelector&&d.addEventListener&&\"undefined\"!=typeof URL&&(d.wp=d.wp||{},d.wp.receiveEmbedMessage||(d.wp.receiveEmbedMessage=function(e){var t=e.data;if((t||t.secret||t.message||t.value)&&!\/[^a-zA-Z0-9]\/.test(t.secret)){for(var s,r,n,a=l.querySelectorAll('iframe[data-secret=\"'+t.secret+'\"]'),o=l.querySelectorAll('blockquote[data-secret=\"'+t.secret+'\"]'),c=new RegExp(\"^https?:$\",\"i\"),i=0;i<o.length;i++)o[i].style.display=\"none\";for(i=0;i<a.length;i++)s=a[i],e.source===s.contentWindow&&(s.removeAttribute(\"style\"),\"height\"===t.message?(1e3<(r=parseInt(t.value,10))?r=1e3:~~r<200&&(r=200),s.height=r):\"link\"===t.message&&(r=new URL(s.getAttribute(\"src\")),n=new URL(t.value),c.test(n.protocol))&&n.host===r.host&&l.activeElement===s&&(d.top.location.href=t.value))}},d.addEventListener(\"message\",d.wp.receiveEmbedMessage,!1),l.addEventListener(\"DOMContentLoaded\",function(){for(var e,t,s=l.querySelectorAll(\"iframe.wp-embedded-content\"),r=0;r<s.length;r++)(t=(e=s[r]).getAttribute(\"data-secret\"))||(t=Math.random().toString(36).substring(2,12),e.src+=\"#?secret=\"+t,e.setAttribute(\"data-secret\",t)),e.contentWindow.postMessage({message:\"ready\",secret:t},\"*\")},!1)))}(window,document);\n\/\/# sourceURL=https:\/\/www.isac.cnr.it\/wp-includes\/js\/wp-embed.min.js\n<\/script>\n","thumbnail_url":"https:\/\/www.isac.cnr.it\/wp-content\/uploads\/2025\/06\/5.png","thumbnail_width":1200,"thumbnail_height":680,"description":"Referente: Daniele Casella Le competenze del Gruppo in ambito Machine Learning sono molto diversificate. Per molti competenti, il Gruppo \u00e8 un\u2019occasione per soddisfare una curiosit\u00e0 su queste tecniche, mentre altri hanno una preparazione solo teorica, alcuni infine hanno un\u2019esperienza diretta nell\u2019applicazione di tecniche ML nell&#8217;ambito della modellistica atmosferica e del telerilevamento (remote sensing) di variabili [&hellip;]"}