Advanced Communication Analysis is a master seminar in which advanced topics in the area of analysis are discussed. The precise topics for the Summer Semester 2025 will be decided upon with the participants.

Many every-day processes can seen as a game between autonomous interacting players, where each player acts strategically in order to pursue her own objectives. This lecture is an introduction to game-theoretic concepts and techniques, mainly with connections to applications. Use-cases are distributed systems, auctions, online-markets, resource allocation, and traffic networks. The goal of the lecture is to provide an overview over state-of-the-art results in the area of algorithmic game theory. Main topics that we will cover in the course are \begin{itemize} \item Strategic Games and Efficiency of Equilibria \item Auctions, Truthfulness and VCG-mechanisms \item Cooperative Games \item Social Choice \end{itemize}

The lectures and homework sheets will be in English language. If all participants agree, the exercise session could be held in German. If there is an oral exam, the language can be chosen by the candidate. In case of a written exam the questions will be in English, answering them in German or English is fine.

Commutative algebra is a branch of algebra which studies commutative rings, ideals and modules. We will discuss the theory of ideals of a polynomial ring, graded and multigraded modules, Gröbner bases, presentations and resolutions of modules. Topics are: Polynomial rings, ideals and varieties Gröbner bases and Buchberger’s algorithm Hilbert’s Nullstellensatz Multigraded modules and Betti numbers Rank invariants Presentations and resolutions Hilbert’s syzygy theorem Schreyer’s theorem

This is the research seminar of our working group. It is open to all students with interest in the mathematical foundation of AI. As prerequisite, the students should have successfully finished an introductory course in data analysis including the basics of neural networks.

In this course we will delve into the fascinating world of Ergodic Theory, a branch of mathematics that studies the asymptotic properties of transformations on topological and measurable spaces. From the origins of the ergodic hypothesis, which laid the foundation for classical statistical mechanics, to modern applications such as hyperbolic geometry or metric number theory, we will uncover the intricate relationships between measure-preserving systems, recurrence, entropy, and stochastic characterisations of dynamical systems. Through a combination of theoretical foundations and illuminating examples, we will explore the fundamental concepts of ergodic theory, including Measure-preserving systems and their properties Several ergodic theorems and their implications Recurrence and its role in understanding the behaviour of dynamical systems Dynamical spectra and their connections to number theory Entropy and its role in the study of dynamical systems Ergodic theory has far-reaching implications in many different fields outside mathematics, including physics, biology, economics and computer science (machine learning). The study of dynamical systems and ergodic theory has led to numerous breakthroughs in mathematics and has been recognised by several Fields Medals in recent years. By studying ergodic theory, you will gain a deeper understanding of the underlying mathematical structures and principles that govern complex systems. This course is an excellent starting point for further research in the field of dynamical systems, leading to exciting topics for master theses in the areas of the analysis/stochastics group. Join us on this journey into the fascinating world of Ergodic Theory!

The lecture addresses Interactive Visualization of Huge Scientific Datasets. More information can also be found on the Homepage: https://www.uni-bremen.de/ag-high-performance-visualization

Die Vorlesung beschäftigt sich mit den mathematischen Grundlagen der wissenschaftlichen Visualisierung und behandelt Methoden für das parallele Post-Processing großer wissenschaftlicher Datensätze. Anwendungsbeispiele werden anhand der Open-Source-Software ParaView erläutert.
Homepage zur Veranstaltung: https://www.uni-bremen.de/ag-high-performance-visualization

Inverse problems are problems where one would like to find an unknown cause for which one can only measure observed effects. This situation occurs, for example, if one can only make indirect measurements of the quantity of interest. Two simple examples: \begin{itemize} \item We measure the position of an object, but would like to know the speed. \item In tomography we measure several projections (X-ray images) of an object, but would like to know the absorption spectrum of said object. \end{itemize} Inverse problems usually suffer from ill-posedness: Solutions may not be unique, they may not exist (for example due to measurement noise), and, most drastically, their solution is unstable in the sense that it does not depend continuously on the data. We will analyze the phenomenon on ill-posedness for linear inverse problems (modeled as linear and continuous maps between Hilbert spaces) to understand the reason for instability. A central goal of the course is to establish the notion of regularization of ill-posed problems (which roughly means the approximate solution by stable methods) and to derive and analyze regularization methods such as Tikhonov regularization, or the Landweber method with early stopping. We will also treat the numerical solution of inverse problems in the lecture and the exercises.

This seminar will treat methods of convex optimization that are suitable for large problems with millions of variables (the size is basically restricted that the computer memory can hold a small finite number of vectors). The guiding principle of these methods is to exploit suitable additive \emph{splitting} of the objective function and then use simple building blocks for the splitted parts to assemble an algorithm. Recently there has been much progress in this areas and in this seminar we will explore the following directions, for example: \begin{itemize} \item Stochastic optimization (stochastic gradient descent, stochastic proximal gradient method), [Chapter 7, 1] \item Accelerated methods (Accelerated gradient descent, accelerated proximal point methods), [Chapter 12, 1] \item Degenerate preconditioned proximal point methods [2] \item Kaczmarz (row-action) methods with mismatched adjoint [3] \end{itemize}

This course introduces quantum computing, covering the mathematical principles needed to understand and implement quantum algorithms. Topics include qubits, entanglement, quantum gates, and circuits. Students will explore algorithms like Deutsch-Jozsa and Shor’s, and problems such as secure communication. The course also addresses error correction and adiabatic algorithms, and includes hands-on practice using Qiskit to build and simulate quantum circuits on IBM’s Quantum Lab.

Die Veranstaltung findet im KKSB Raum 40010 statt.

Die Veranstaltung findet im KKSB LINZ 4 statt.

Gießen bedeutet die Herstellung eines endformnahen Bauteils aus dem schmelzflüssigen Zustand. Das Verfahren an sich ist seit Jahrtausenden bekannt - dennoch ist Gießen keine altertümliche Technologie. Gerade in der Automobilindustrie besitzt es einen hohen Stellenwert. Je nach Seriengröße und Art der Bauteile kommen unterschiedlichste Gießverfahren vom Sand- und Kokillenguss bis zum Niederdruck- und Druckguss zur Anwendung, während im Bereich der Werkstoffe Aluminiumlegierungen dominieren. Gleichzeitig steht die Gießereiindustrie jedoch vor massiven Herausforderungen: So verändert die Hinwendung der OEMs zur Elektromobilität das Produktspektrum und damit die Anforderungen an die Gussteile. Dies führt zu einer verstärkten Hinwendung zu strukturellen Anwendungen für Gussteile, die weiter befeuert wird durch das von Fa. Tesla erstmals eingeführte Gigacasting, also die Zusammenführung einer Vielzahl gefügter Komponenten in einem einzigen Großgussbauteil. Nicht zuletzt diese Technologie erfordert eine weiter optimierte Überwachung und Steuerung der Gießprozesse. Damit wächst auch für die Gießereiindustrie der Druck, sich mit Themen wie Industrie 4.0 und datengetriebener Prozessoptimierung etwa mittels KI-Verfahren zu beschäftigen. Die Vorlesung vermittelt einen Überblick über die Gießereitechnologie mit einem besonderen Fokus auf Anwendungen im Automobil unter Berücksichtigung aktueller Trends. Eine Anbindung an die Praxis erfolgt über die Besichtigung des Gießereitechnikums des Fraunhofer IFAM sowie, soweit möglich, über eine Übung im Bereich Gießsimulation mit der Simulationssoftware MAGMASOFT®.

Schwerpunkt: AI
https://lvb.informatik.uni-bremen.de/ibap/03-ibap-ml.pdf
Die Übungen starten in der 2. Semesterwoche.
IBAP-MLd (German) or IBAP-MLe (English) can be taken, but the course can only be credited once!

Profil: KIKR, DMI
Schwerpunkt: IMA-AI, IMVP-DMI, IMVP-VMC
https://lvb.informatik.uni-bremen.de/imap/03-imap-uuw.pdf

Gießen bedeutet die Herstellung eines endformnahen Bauteils aus dem schmelzflüssigen Zustand. Das Verfahren an sich ist seit Jahrtausenden bekannt - dennoch ist Gießen keine altertümliche Technologie. Gerade in der Automobilindustrie besitzt es einen hohen Stellenwert. Je nach Seriengröße und Art der Bauteile kommen unterschiedlichste Gießverfahren vom Sand- und Kokillenguss bis zum Niederdruck- und Druckguss zur Anwendung, während im Bereich der Werkstoffe Aluminiumlegierungen dominieren. Gleichzeitig steht die Gießereiindustrie jedoch vor massiven Herausforderungen: So verändert die Hinwendung der OEMs zur Elektromobilität das Produktspektrum und damit die Anforderungen an die Gussteile. Dies führt zu einer verstärkten Hinwendung zu strukturellen Anwendungen für Gussteile, die weiter befeuert wird durch das von Fa. Tesla erstmals eingeführte Gigacasting, also die Zusammenführung einer Vielzahl gefügter Komponenten in einem einzigen Großgussbauteil. Nicht zuletzt diese Technologie erfordert eine weiter optimierte Überwachung und Steuerung der Gießprozesse. Damit wächst auch für die Gießereiindustrie der Druck, sich mit Themen wie Industrie 4.0 und datengetriebener Prozessoptimierung etwa mittels KI-Verfahren zu beschäftigen. Die Vorlesung vermittelt einen Überblick über die Gießereitechnologie mit einem besonderen Fokus auf Anwendungen im Automobil unter Berücksichtigung aktueller Trends. Eine Anbindung an die Praxis erfolgt über die Besichtigung des Gießereitechnikums des Fraunhofer IFAM sowie, soweit möglich, über eine Übung im Bereich Gießsimulation mit der Simulationssoftware MAGMASOFT®.

What will I learn? In this course, you will receive expert input, write your own blog post, and get feedback. Successful blog posts will be published on the University of Bremen's Science Blog.

Why Science Blogging? Science Blogging is a powerful tool in digital Science Communication. Communicating science helps bridging the gap between specialists and non-specialists. Sharing research topics from the University of Bremen beyond campus enriches public understanding and appreciation of science. Furthermore, unlike fleeting social media posts, blog entries remain accessible online for long periods.

What can I write about? Topics for your blog post should be connected to the University of Bremen and can e.g. include your own reasearch project, an interview or lab tour with a researcher, an article about a research project or working group. You can write in English or German.

What are the requirements? We require participation in the three meetings on campus and the submission of your  blog article,  reviews of two other blog articles and to revise and finalize your own blog article.

How many CPs can I get for this General Studies course? You receive 1CP for this course (“unbenotete Studienleistung”). In the winter term 2025/26 and in the summer term 2026 we will additionally offer the course “Science Blog Editorial Team”. With this course you can join the active work in the editorial team and gain 1 up to 2 additional CPs.

Where can I register? Find the course here in Stud IP.

Please contact us with any questions: Hannah Zindel, mapex-communicationprotect me ?!uni-bremen.de.