<?xml version="1.0" encoding="UTF-8"?><rss xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:content="http://purl.org/rss/1.0/modules/content/" xmlns:atom="http://www.w3.org/2005/Atom" version="2.0"><channel><title><![CDATA[Aeris-UK]]></title><description><![CDATA[We specialise in AI & modelling solutions that overcome speed, cost & power limits to deliver decision support in defence, aerospace & infrastructure.]]></description><link>https://www.ukaeris.com/insights</link><generator>RSS for Node</generator><lastBuildDate>Tue, 29 Sep 2026 14:50:19 GMT</lastBuildDate><atom:link href="https://www.ukaeris.com/blog-feed.xml" rel="self" type="application/rss+xml"/><item><title><![CDATA[From theory to edge AI: Ben Chang on his summer at Aeris-UK]]></title><description><![CDATA[Cambridge engineer Ben Chang reflects on his summer internship at Aeris-UK, working across edge AI, real-world data and client-facing R&#38;D, and how the experience shaped his plans for a career in AI.]]></description><link>https://www.ukaeris.com/post/ben-chang-edge-ai-internship</link><guid isPermaLink="false">6abaf06ec783f93c683bd545</guid><category><![CDATA[Perspectives]]></category><category><![CDATA[Research & Engineering]]></category><pubDate>Mon, 28 Sep 2026 23:21:19 GMT</pubDate><enclosure url="https://static.wixstatic.com/media/74ee8e_e9f2feddf56342b094b2f89baa679ed7~mv2.jpg/v1/fit/w_1000,h_1000,al_c,q_80/file.png" length="0" type="image/png"/><dc:creator>Natasha Zheltovskaya</dc:creator></item><item><title><![CDATA[Aeris-UK at AMLUCS 2026: agentic cyber defence at the tactical edge]]></title><description><![CDATA[Aeris-UK presented its work on agentic cyber defence at the land tactical edge and demonstrated the CEDAR prototype live at AMLUCS 2026.]]></description><link>https://www.ukaeris.com/post/agentic-cyber-defence-amlucs-2026</link><guid isPermaLink="false">6abae3f0f3c2882f246cf07d</guid><category><![CDATA[News & Updates]]></category><category><![CDATA[Research & Engineering]]></category><pubDate>Mon, 28 Sep 2026 22:33:14 GMT</pubDate><enclosure url="https://static.wixstatic.com/media/74ee8e_72179d88bb79410187bfa24068d976d0~mv2.jpg/v1/fit/w_1000,h_1000,al_c,q_80/file.png" length="0" type="image/png"/><dc:creator>Natasha Zheltovskaya</dc:creator></item><item><title><![CDATA[How Small AI Companies Can Train Models Sustainably — Every Little Bit Helps!]]></title><description><![CDATA[Training AI models can be computationally expensive, particularly for smaller teams. This article examines practical ways to reduce energy use and training costs while maintaining effective model performance.]]></description><link>https://www.ukaeris.com/post/how-small-ai-companies-can-train-models-sustainably-every-little-bit-helps</link><guid isPermaLink="false">6aba458fe3d65231930a5dfc</guid><category><![CDATA[Perspectives]]></category><category><![CDATA[Research & Engineering]]></category><pubDate>Sun, 27 Sep 2026 23:00:00 GMT</pubDate><enclosure url="https://static.wixstatic.com/media/74ee8e_1b64e2394e13413c88e9dbdf97ac43ed~mv2.webp/v1/fit/w_1000,h_1000,al_c,q_80/file.png" length="0" type="image/png"/><dc:creator>Kelvin Yeung</dc:creator></item></channel></rss>