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	<title>FAU DCN-AvH</title>
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	<description>Chair for Dynamics, Control, Machine Learning and Numerics -Alexander von Humboldt Professorship</description>
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	<title>FAU DCN-AvH</title>
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		<title>PhD Thesis defense by Z. Wang</title>
		<link>https://dcn.nat.fau.eu/phd-thesis-defense-by-z-wang/</link>
		
		<dc:creator><![CDATA[darlis.dcn]]></dc:creator>
		<pubDate>Wed, 14 Oct 2026 10:32:16 +0000</pubDate>
				<category><![CDATA[2026-resources]]></category>
		<category><![CDATA[EZuazua]]></category>
		<category><![CDATA[EZuazua Akademy]]></category>
		<category><![CDATA[Resources]]></category>
		<category><![CDATA[Thesis]]></category>
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					<description><![CDATA[This year our team member Ziqi Wang will present his PhD Thesis on: &#8220;Federated Learning under Heterogeneity: Efficiency, Fairness, and Safety&#8221; Advisor: • Prof. Enrique Zuazua, FAU &#8211; Friedrich Alexander-Universität Erlangen-Nürnberg (Germany) Abstract. Machine learning has become central to modern science and technology, and its performance depends heavily on diverse training datasets. However, such data [&#8230;]]]></description>
		
		
		
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		<title>PhD Thesis defense by A. Alcalde</title>
		<link>https://dcn.nat.fau.eu/phd-thesis-defense-by-a-alcalde/</link>
		
		<dc:creator><![CDATA[darlis.dcn]]></dc:creator>
		<pubDate>Fri, 09 Oct 2026 17:30:16 +0000</pubDate>
				<category><![CDATA[2026-resources]]></category>
		<category><![CDATA[EZuazua]]></category>
		<category><![CDATA[EZuazua Akademy]]></category>
		<category><![CDATA[Resources]]></category>
		<category><![CDATA[Thesis]]></category>
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					<description><![CDATA[Next Monday October 26, 2026 our team member Albert Alcalde will present his PhD Thesis on: &#8220;A Dynamical Perspective on Transformers, with Applications to Data-Driven Modeling&#8221; Advisors: • Prof. Giovanni Fantuzzi, FAU &#8211; Friedrich Alexander-Universität Erlangen-Nürnberg (Germany) • Prof. Enrique Zuazua, FAU &#8211; Friedrich Alexander-Universität Erlangen-Nürnberg (Germany) Abstract. Transformers form a central class of architectures [&#8230;]]]></description>
		
		
		
			</item>
		<item>
		<title>Postdoctoral Researcher in Applied Mathematics</title>
		<link>https://dcn.nat.fau.eu/careers-open-position-postdoc-oct2026/</link>
		
		<dc:creator><![CDATA[darlis.dcn]]></dc:creator>
		<pubDate>Tue, 06 Oct 2026 21:03:55 +0000</pubDate>
				<category><![CDATA[Jobs]]></category>
		<category><![CDATA[Join Us]]></category>
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					<description><![CDATA[Postdoctoral Researcher in Applied Mathematics Control, Dynamical Systems and Machine Learning ERC Advanced Grant CoDeFeL Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU) Erlangen, Germany The Chair for Dynamics, Control, Machine Learning, and Numerics – Alexander von Humboldt Professorship (FAU DCN-AvH), led by Prof. Enrique Zuazua, invites applications for a postdoctoral researcher position in applied mathematics within the ERC Advanced [&#8230;]]]></description>
		
		
		
			</item>
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		<title>FAU MoD Course: Discovery of singularities with physics-informed neural networks</title>
		<link>https://dcn.nat.fau.eu/fau-mod-course-discovery-of-singularities-with-physics-informed-neural-networks/</link>
		
		<dc:creator><![CDATA[darlis.dcn]]></dc:creator>
		<pubDate>Tue, 06 Oct 2026 19:44:37 +0000</pubDate>
				<category><![CDATA[2025-resources]]></category>
		<category><![CDATA[FAU MoD]]></category>
		<category><![CDATA[FAUDCNSeminar]]></category>
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					<description><![CDATA[Date: Wed. &#8211; Fri. December 02 &#8211; 04, 2026 Event: FAU MoD Course Organizer: FAU MoD, Research Center for Mathematics of Data at FAU, Friedrich-Alexander-Universität Erlangen-Nürnberg FAU MoD Course: Discovery of singularities with physics-informed neural networks Speaker: Prof. Dr. Gonzalo Cao-Labora Affiliation: EPFL, École Polytechnique Fédérale de Lausanne Abstract. The course will be concerned with [&#8230;]]]></description>
		
		
		
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		<title>FAU MoD Lecture: Moving your robotic hand is a matter of time and practice</title>
		<link>https://dcn.nat.fau.eu/fau-mod-lecture-moving-your-robotic-hand-is-a-matter-of-time-and-practice/</link>
		
		<dc:creator><![CDATA[darlis.dcn]]></dc:creator>
		<pubDate>Tue, 06 Oct 2026 19:43:26 +0000</pubDate>
				<category><![CDATA[2025-resources]]></category>
		<category><![CDATA[FAU MoD]]></category>
		<category><![CDATA[FAUDCNSeminar]]></category>
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					<description><![CDATA[Date: Mon. November 23, 2026 Event: FAU MoD Lecture Organized by: FAU MoD, the Research Center for Mathematics of Data at Friedrich-Alexander-Universität Erlangen-Nürnberg (Germany) FAU MoD Lecture: Moving your robotic hand is a matter of time and practice Speaker: Prof. Dr. Claudio Castellini Affiliation: The Assistive Intelligent Robotics Laboratory. FAU, Friedrich-Alexander-Universität Erlangen-Nürnberg (Germany) Abstract. Assistive [&#8230;]]]></description>
		
		
		
			</item>
		<item>
		<title>FAU MoD Lecture: Trustworthy AI: Reliable, Explainable, Revisable</title>
		<link>https://dcn.nat.fau.eu/fau-mod-lecture-trustworthy-ai-reliable-explainable-revisable/</link>
		
		<dc:creator><![CDATA[darlis.dcn]]></dc:creator>
		<pubDate>Tue, 06 Oct 2026 19:42:07 +0000</pubDate>
				<category><![CDATA[2025-resources]]></category>
		<category><![CDATA[FAU MoD]]></category>
		<category><![CDATA[FAUDCNSeminar]]></category>
		<guid isPermaLink="false">https://dcn.nat.fau.eu/?p=33700</guid>

					<description><![CDATA[Date: Mon. November 16, 2026 Event: FAU MoD Lecture Organized by: FAU MoD, the Research Center for Mathematics of Data at Friedrich-Alexander-Universität Erlangen-Nürnberg (Germany) FAU MoD Lecture: Trustworthy AI: Reliable, Explainable, Revisable Speaker: Prof. Dr. Ute Schmid Affiliation: Cognitive Systems Group, University of Bamberg and Bamberg Center of Artificial Intelligence (BaCAI), Germany Abstract. With the [&#8230;]]]></description>
		
		
		
			</item>
		<item>
		<title>Course: Mathematical Foundations of Control and Machine Learning</title>
		<link>https://dcn.nat.fau.eu/course-mathematical-foundations-of-control-and-machine-learning/</link>
		
		<dc:creator><![CDATA[darlis.dcn]]></dc:creator>
		<pubDate>Mon, 05 Oct 2026 11:47:04 +0000</pubDate>
				<category><![CDATA[2026-resources]]></category>
		<category><![CDATA[Akademy]]></category>
		<category><![CDATA[Akademy Daniel López Montero]]></category>
		<category><![CDATA[Akademy Enrique Zuazua]]></category>
		<category><![CDATA[Course]]></category>
		<category><![CDATA[Resources]]></category>
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					<description><![CDATA[Mathematical Foundations of Control and Machine Learning Master Course – FAU, Friedrich-Alexander-Universität Erlangen–Nürnberg Date: Winter Semester 2026/2027 Course Overview The course provides a unified mathematical perspective on control theory and machine learning. It introduces key concepts from the analysis of dynamical systems governed by ordinary and partial differential equations (ODEs and PDEs) — controllability, observability [&#8230;]]]></description>
		
		
		
			</item>
		<item>
		<title>Industrial &#038; Social Transference</title>
		<link>https://dcn.nat.fau.eu/transference/</link>
		
		<dc:creator><![CDATA[darlis.dcn]]></dc:creator>
		<pubDate>Thu, 01 Oct 2026 20:29:47 +0000</pubDate>
				<category><![CDATA[CoDeFeL]]></category>
		<category><![CDATA[CoDeFeL GitHub]]></category>
		<guid isPermaLink="false">https://dcn.nat.fau.eu/?p=33665</guid>

					<description><![CDATA[]]></description>
		
		
		
			</item>
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		<title>CoDeFeL • GitHub</title>
		<link>https://dcn.nat.fau.eu/codefel-github/</link>
		
		<dc:creator><![CDATA[darlis.dcn]]></dc:creator>
		<pubDate>Thu, 01 Oct 2026 20:22:49 +0000</pubDate>
				<category><![CDATA[CoDeFeL]]></category>
		<category><![CDATA[CoDeFeL GitHub]]></category>
		<guid isPermaLink="false">https://dcn.nat.fau.eu/?p=33656</guid>

					<description><![CDATA[Where mathematics becomes executable innovation. Explore the open repository for the project algorithms, software environment, and reproducible computational research and tools connecting at the interface of Control and Machine Learning. From robust deep-learning methods and federated learning to hybrid ResNet/PDE models. Designed for reproducible research and real-world impact, CoDeFeL equips researchers and practitioners to address [&#8230;]]]></description>
		
		
		
			</item>
		<item>
		<title>CoDeFeL • WP4</title>
		<link>https://dcn.nat.fau.eu/codefel-wp4/</link>
		
		<dc:creator><![CDATA[darlis.dcn]]></dc:creator>
		<pubDate>Thu, 01 Oct 2026 20:00:03 +0000</pubDate>
				<category><![CDATA[CoDeFeL]]></category>
		<category><![CDATA[CoDeFeL WP]]></category>
		<guid isPermaLink="false">https://dcn.nat.fau.eu/?p=33651</guid>

					<description><![CDATA[WP4 &#8211; Modelling through Control and Machine Learning Control theory provides a principled framework for regulating dynamical systems, while Machine Learning enables complex patterns and predictive models to be extracted from data. By integrating these two fields, we develop efficient, robust, and adaptive methodologies that combine data-driven learning with feedback mechanisms. This synergy provides a [&#8230;]]]></description>
		
		
		
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