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	<description>Chair for Dynamics, Control, Machine Learning and Numerics -Alexander von Humboldt Professorship</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>
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		<category><![CDATA[EZuazua Akademy]]></category>
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		<category><![CDATA[Thesis]]></category>
		<guid isPermaLink="false">https://dcn.nat.fau.eu/?p=33747</guid>

					<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>
		
		
		
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		<title>PhD Thesis defense by A. Alvarez-Lopez</title>
		<link>https://dcn.nat.fau.eu/phd-thesis-defense-by-a-alvarez-lopez/</link>
		
		<dc:creator><![CDATA[darlis.dcn]]></dc:creator>
		<pubDate>Mon, 29 Jun 2026 17:33:28 +0000</pubDate>
				<category><![CDATA[2026-resources]]></category>
		<category><![CDATA[EZuazua]]></category>
		<category><![CDATA[EZuazua Akademy]]></category>
		<category><![CDATA[Resources]]></category>
		<category><![CDATA[Thesis]]></category>
		<guid isPermaLink="false">https://dcn.nat.fau.eu/?p=33046</guid>

					<description><![CDATA[Next Wednesday July 1, 2026 our team member Antonio Álvarez-López will present his PhD Thesis on: &#8220;Deep Learning with Controlled Flows: Expressivity, Generalization and Generation&#8221; Advisors: • Prof. Enrique Zuazua, FAU &#8211; Friedrich Alexander-Universität Erlangen-Nürnberg (Germany) • Prof. Rafael Orive, Autonomous University of Madrid (UAM) Abstract. This thesis studies deep learning from the perspective of [&#8230;]]]></description>
		
		
		
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		<title>Synthetic profile generation for energy system planning using Artificial Intelligence</title>
		<link>https://dcn.nat.fau.eu/synthetic-profile-generation-for-energy-system-planning-using-artificial-intelligence/</link>
		
		<dc:creator><![CDATA[darlis.dcn]]></dc:creator>
		<pubDate>Mon, 01 Jun 2026 19:58:04 +0000</pubDate>
				<category><![CDATA[2023-resources]]></category>
		<category><![CDATA[EZuazua]]></category>
		<category><![CDATA[EZuazua Akademy]]></category>
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		<category><![CDATA[Thesis]]></category>
		<guid isPermaLink="false">https://dcn.nat.fau.eu/?p=32933</guid>

					<description><![CDATA[Master Thesis: &#8220;Synthetic profile generation for energy system planning using Artificial Intelligence&#8221; Author: Aniruddha Maiti Supervisors: Prof. Enrique Zuazua, Zhengping Ji Date: June, 2026 Rapid urbanization and population growth have led to a significant increase in energy consumption, especially in densely populated metropolitan areas. Simultaneously, the European Union and Germany have set ambitious goals to [&#8230;]]]></description>
		
		
		
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		<title>Robust Creep Control Algorithm with motion shaping and tracking for passenger vehicles</title>
		<link>https://dcn.nat.fau.eu/robust-creep-control-algorithm-with-motion-shaping-and-tracking-for-passenger-vehicles/</link>
		
		<dc:creator><![CDATA[darlis.dcn]]></dc:creator>
		<pubDate>Mon, 01 Jun 2026 03:13:51 +0000</pubDate>
				<category><![CDATA[2023-resources]]></category>
		<category><![CDATA[Akademy]]></category>
		<category><![CDATA[EZuazua]]></category>
		<category><![CDATA[EZuazua Akademy]]></category>
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		<category><![CDATA[Thesis]]></category>
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					<description><![CDATA[Master Thesis: &#8220;Robust Creep Control Algorithm with motion shaping and tracking for passenger vehicles&#8221; Author: Dante Amor Supervisors: Prof. Enrique Zuazua, Zhengping Ji Date: May, 2026 This thesis aims to provide a new architecture that provides torque commands to control the vehicle, not only reaching a target velocity, but by shaping the full trajectory of [&#8230;]]]></description>
		
		
		
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		<title>Development of a Modular Multi-Agent System Architecture for Enhanced Flexibility and Scalability</title>
		<link>https://dcn.nat.fau.eu/development-of-a-modular-multi-agent-system-architecture-for-enhanced-flexibility-and-scalability/</link>
		
		<dc:creator><![CDATA[darlis.dcn]]></dc:creator>
		<pubDate>Sun, 30 Nov 2025 22:13:06 +0000</pubDate>
				<category><![CDATA[2023-resources]]></category>
		<category><![CDATA[Akademy]]></category>
		<category><![CDATA[EZuazua]]></category>
		<category><![CDATA[EZuazua Akademy]]></category>
		<category><![CDATA[Resources]]></category>
		<category><![CDATA[Thesis]]></category>
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					<description><![CDATA[Master Thesis: &#8220;Development of a Modular Multi-Agent System Architecture for Enhanced Flexibility and Scalability&#8221; Author: Greshma Shaji Supervisors: Ziqi Wang, Prof. Enrique Zuazua, Prof. Frauke Liers Date: November, 2025 See all details at master thesis: &#8220;Development of a Modular Multi-Agent System Architecture for Enhanced Flexibility and Scalability&#8221;, by Greshma Shaji (November, 2025) Large Language Models [&#8230;]]]></description>
		
		
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		<title>PhD Thesis defense by M. Hernández Salinas</title>
		<link>https://dcn.nat.fau.eu/phd-thesis-defense-mhernandez/</link>
		
		<dc:creator><![CDATA[darlis.dcn]]></dc:creator>
		<pubDate>Wed, 24 Sep 2025 09:12:18 +0000</pubDate>
				<category><![CDATA[2025-resources]]></category>
		<category><![CDATA[Akademy Martin Hernandez]]></category>
		<category><![CDATA[EZuazua]]></category>
		<category><![CDATA[EZuazua Akademy]]></category>
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		<category><![CDATA[Thesis]]></category>
		<guid isPermaLink="false">https://dcn.nat.fau.eu/?p=31799</guid>

					<description><![CDATA[Next Thursday September 25, 2025 our team member Martín Hernández will present his PhD Thesis on: &#8220;From Optimal Control to Random and Neural Network Approximation&#8221; Supervisor: Prof. Enrique Zuazua, FAU &#8211; Friedrich Alexander-Universität Erlangen-Nürnberg (Germany) Abstract. This talk presents contributions grounded in control and computation. First, we focus on long-time optimal control, particularly the so-called [&#8230;]]]></description>
		
		
		
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		<title>AI-based Diagnosis of Combustion Anomalies in Hydrogen Engines</title>
		<link>https://dcn.nat.fau.eu/ai-based-diagnosis-of-combustion-anomalies-in-hydrogen-engines/</link>
		
		<dc:creator><![CDATA[darlis.dcn]]></dc:creator>
		<pubDate>Tue, 30 Jul 2024 11:13:51 +0000</pubDate>
				<category><![CDATA[2023-resources]]></category>
		<category><![CDATA[EZuazua]]></category>
		<category><![CDATA[EZuazua Akademy]]></category>
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		<category><![CDATA[Thesis]]></category>
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					<description><![CDATA[Master Thesis: &#8220;AI-based Diagnosis of Combustion Anomalies in Hydrogen Engines&#8221; Author: Arun Sai Thunga Supervisor: Prof. Enrique Zuazua Date: June 30, 2024 In the field of automobile maintenance, a wide range of sensors are useful for capturing the signals inside the combustion chamber of engines. Addressing the intricacies of anomaly detection in H2 engine combustion [&#8230;]]]></description>
		
		
		
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		<title>Synthetic Training Data Generation for Point Cloud Defects</title>
		<link>https://dcn.nat.fau.eu/synthetic-training-data-generation-for-point-cloud-defects/</link>
		
		<dc:creator><![CDATA[darlis.dcn]]></dc:creator>
		<pubDate>Mon, 29 Jan 2024 12:13:29 +0000</pubDate>
				<category><![CDATA[2023-resources]]></category>
		<category><![CDATA[EZuazua]]></category>
		<category><![CDATA[EZuazua Akademy]]></category>
		<category><![CDATA[Resources]]></category>
		<category><![CDATA[Thesis]]></category>
		<guid isPermaLink="false">https://dcn.nat.fau.eu/?p=29741</guid>

					<description><![CDATA[Master Thesis: &#8220;Synthetic Training Data Generation for Point Cloud Defects&#8221; Author: Nishitha Yedalapalli Supervisor: Prof. Enrique Zuazua, Yongcun Song Date: January 29, 2024 In the field of aviation maintenance, white light interferometry is useful for detecting submillimeter cracks within combustion chambers. This cutting-edge technology gen- erates high-resolution point cloud data that captures fine surface features. [&#8230;]]]></description>
		
		
		
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		<title>Analysis, control, and singular limits for hyperbolic conservation laws</title>
		<link>https://dcn.nat.fau.eu/phd-thesis-defense-by-n-de-nitti/</link>
		
		<dc:creator><![CDATA[darlis.dcn]]></dc:creator>
		<pubDate>Mon, 24 Jul 2023 11:13:03 +0000</pubDate>
				<category><![CDATA[2023-resources]]></category>
		<category><![CDATA[Akademy Nicola De Nitti]]></category>
		<category><![CDATA[EZuazua]]></category>
		<category><![CDATA[EZuazua Akademy]]></category>
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		<category><![CDATA[Thesis]]></category>
		<guid isPermaLink="false">https://dcn.nat.fau.eu/?p=26940</guid>

					<description><![CDATA[Analysis, control, and singular limits for hyperbolic conservation laws Author: Nicola de Nitti Date: Monday July 24, 2023 PhD Thesis: Analysis, control, and singular limits for hyperbolic conservation laws (July 24, 2023) Abstract. The main focus of this thesis is on the study of singular limits related to scalar conservation laws. These are first-order partial [&#8230;]]]></description>
		
		
		
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		<item>
		<title>Machine Learning: Publisher Recommendation Model</title>
		<link>https://dcn.nat.fau.eu/machine-learning-publisher-recommendation-model/</link>
		
		<dc:creator><![CDATA[darlis.dcn]]></dc:creator>
		<pubDate>Fri, 07 Jul 2023 18:34:25 +0000</pubDate>
				<category><![CDATA[2023-resources]]></category>
		<category><![CDATA[EZuazua]]></category>
		<category><![CDATA[EZuazua Akademy]]></category>
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		<category><![CDATA[Thesis]]></category>
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					<description><![CDATA[Master Thesis: &#8220;Machine Learning: Publisher Recommendation Model&#8221; Author: Mohammed Quaidjohar Husain Supervisor: Prof. Dr. DhC. Enrique Zuazua Date: July 7, 2023 The exponential rise of digital content in recent years, along with the spread of inter- net platforms, has led to an excessive influx of data from all sources, which causes an information overload issue. [&#8230;]]]></description>
		
		
		
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