Germany

German academic writing is a skill that can be learned. By engaging with selected modern literary texts in the writing lab, students practice to develop research questions, prepare outlines, draft exposés, construct arguments, and comment on academic positions. The goal of the course is to enable participants to prepare well-structured term papers, bachelor's or master's theses, dissertations, and presentations. It also address the grammatical and
stylistic peculiarities of the German academic language, including intercultural distinctions. Moreover, students investigate the promise, perils, and limitations of artificial intelligence (AI), and the extent to which AI can facilitate many areas of academic work but not replace the need for critical and innovative thinking. By the end of the course, participants are equipped to successfully stand their ground in German academic discourse. At the same time, they acquire transferable skills to write clearly structured, concise academic texts in their own language.

Language anxiety and linguistic insecurity are central topics in multilingual and transcultural contexts. In this seminar, students investigate the causes and effects of language anxiety, in language acquisition as well as in the day to day. The class looks at different forms of linguistic insecurity and language anxiety that are affected by social norms, language ideologies, and individual experiences. The goal of the seminar is to develop a critical understanding of this phenomenon and how to approach linguistic insecurity. The readiness to work with research literature in English is required. Students need to take this seminar alongside the lecture "Second Language Acquisition and Multilingualism".

The course introduces the basics of Geometry Processing. It presents mathematical models, data structures and algorithms to represent geometry on modern computer applications, and these are manipulated through practical exercises. The techniques seen in the course are fundamental for applications like 3D modeling, geometry reconstruction from scanned objects, and physical simulation.

In this course, students gain an integrative understanding of the field of Artificial Intelligence (AI), with equal emphasis on data-driven AI (especially machine learning) and model-based AI (especially planning and reasoning). They come to understand AI from the perspectives of decision theory, machine learning, optimization, and classical problem solving. Students learn to independently implement and understand core algorithms from these areas and can identify appropriate problem formulations and AI algorithms for a given application. Course topics include problem formulations and algorithmic approaches from decision theory (including reinforcement learning, multi-armed bandits, control theory), machine learning, optimization, and inference, classical planning, and problem solving. The class also discusses fundamental and recurring algorithmic principles such as dynamic programming, optimization-based vs. sampling-based methods, and decision trees.

 

In the last two decades, an increasing number of artists have engaged the specters of colonialism that continue to haunt us in our postcolonial present. In their work, the archive often figures as source or resource, matter or metaphor, and presence or absence of the colonial past. Considering the intensity of this archival return, it is no exaggeration to state that the archive has emerged as a paradigm through which artists pursue engagements with colonial histories. In their work the archive enables them to confront the legacies of their colonial pasts and provides them with possibilities to conceptualize the hidden histories and counter-memories that have been suppressed by screen memories whose traumatic contents need to be addressed to open up alternative futures. Conventionally imagined as a technology for the storage of traces of the past, in this context the archive may be thought of as a site to rethink the past, present, and future. This seminar examines how work in the archive explores alternative relations between past, present and future. This is done by examining a range of practices adopted by scholars, archivists, social activists, and contemporary artists in their engagement with the archive. This includes themes like; how colonial archives have been neglected, destroyed, and replaced by decolonial archives; how photographers have embraced archival images as material to recycle and repurpose; how contemporary artists have developed alternative archival epistemologies; how restitution might be conceived as a form of archival memory work; and why, in the post-apartheid context in South Africa, the decolonization of the university has been conceived as a question of the archive. In sum, the seminar examines how the archival turn addresses the question of African futures.

This course consists of practical project work or audit and an evaluation field stay including technologies and background information necessary to develop sustainable community-based projects, e.g. PV training, CO2compensation, household biogas plants, clean cooking, biogas, income generation. International student hybrid working groups develop CO2 compensation projects for climate and SDGs tackling the needs of the local partner communities together with the partner NGOs. The course offers research and innovation opportunities to deepen the development and application of sustainable technologies and methodologies. It also includes cooperation with local community organizations, NGOs, international universities, and other partners.

This course's aim is to develop an understanding of large language models (LLM) as well as their evaluation and to practice reading, understanding, and presenting research work. As part of the class, methods for the selection of data and LLMs, data preparation, application and evaluation of LLMs are developed and put into practice. The use cases are examples from the field of natural language processing, e.g. translation, summary of text, or information extraction.

This course discusses differential geometry of curves and surfaces in Euclidian Space: curves in 2- and 3-dimensional spaces, local and global theory of surfaces, special classes of surfaces, discrete curves and surfaces.

The course explores the long-term socio-economic consequences of exposure to natural disasters, focusing on the level of the individual. It consists of two complementary classes that have to be taken together. The first part of the course provides students with a theoretical foundation for understanding how natural disasters can shape economic and social outcomes over time. It focuses on discussing channels and mechanisms through which the natural environment and disasters or upheaval, in particular, affect individuals. Topics covered include the impact of such disasters on health, education, household income, labor markets, and migration. Students familiarize themselves with underlying microeconomic models, discuss research methods like causal inference strategies, and analyze empirical findings from academic research. The second part of the course is designed to deepen students’ understanding of the concepts covered in class through active engagement with empirical studies. Students are required to present and critically discuss academic papers that investigate natural disaster effects using micro-level data. The seminar emphasizes methodological approaches, data sources, and empirical strategies, encouraging students to evaluate the presented research critically and develop their analytical skills.