EVENTS

As preparations begin for the pilot launch in South Africa, Kenya, Côte d’Ivoire, Ghana, and Nigeria, this two-hour orientation and awareness session provides a key introduction for educators to the AI and Robotics Youth Training Programme and its specialized Training of Trainers methodology.
The session presents a high-impact learning model built around an equal balance of theory and practice. Through this approach, teachers are guided across the full engineering journey, beginning with foundational AI literacy and advancing toward the design and construction of an autonomous robot to address predefined problem statements. Structured through intensive eight-hour training sessions, the programme gradually equips educators with the knowledge, tools, and confidence needed to support students in developing solutions for a prototype challenge aligned with the ITU Robotics for Good Youth Challenge.
By connecting technical learning with real-world problem-solving, the session seeks to ensure that educators are strategically prepared to transform their schools into spaces of sustained creativity, experimentation, and innovation. Drawing on Giga’s connectivity, Google’s technical expertise, and the research leadership of IHFC-IIT Delhi, the programme aims to empower a new generation of young African innovators and problem-solvers.

Institutions

• AI for Good

Prof. Dr. Özgür Özçep, Universität Hamburg, CHAI

Abstract:
Analogies and analogical reasoning are central topics at the intersection of various disciplines such as computer science (in particular AI), legal reasoning, cognitive science, psychology, and linguistics. This talk focuses on the model of analogical proportions understood as quaternary relations of the form a:b::c:d, read as “a is to b as c is to d.” After presenting recent general results on analogical proportions, the talk discusses their application to concrete instances in two subareas of AI: (1) mechanism design for fair resource allocation and (2) geometry-based knowledge graph embeddings. 

Institution

• UHH

• UHH

Vortrag: Dr. Jonas Müller-Laackman, SUB Hamburg

Presentation: Prof. Dr. Ralf Möller, Institute of Humanities-Centered Artificial Intelligence (in English)

This workshop provides a hands-on introduction to applying vision foundation models (VFMs) in digital pathology, with techniques that are broadly applicable across other image-based domains. VFMs are large-scale vision architectures pre-trained on diverse, high-volume whole slide image corpora to produce reusable visual representations that capture broadly applicable semantic and structural information across image domains. In digital pathology, VFMs are trained on massive collections of histopathology image patches, claiming robust, domain-specific feature extraction. In this workshop, we utilize fixed pre-trained embeddings to study downstream prediction tasks, explicitly focusing on the representational capacity of foundation models. The emphasis lies on keeping model development cycles short and on producing meaningful results on relevant downstream tasks in digital pathology.

Topics: 

• Vision foundation models for digital pathology: overview of pretrained VFM architectures (with a focus on UNI2) and their representational properties in histopathology.
• Fixed-feature learning and embedding-based pipelines: use of frozen VFM embeddings for downstream prediction, avoiding end-to-end retraining while enabling rapid experimentation.
• Downstream evaluation: practical application of VFM embeddings to digital pathology tasks, with emphasis on reproducibility and efficient model development cycles.

Sie haben Sprachaufnahmen für einen Podcast erstellt, sind aber mit der Tonqualität unzufrieden? Es sind Störgeräusche zu hören, die entfernt werden sollen? Oder Sie benötigen ein Transkript Ihrer Sprachaufnahme?

In dieser 30-minütigen Online-Schulung erhalten Sie einen schnellen Überblick über die Möglichkeiten, Podcast-Aufnahmen mithilfe von KI in Audacity zu bearbeiten.
Sie lernen die kostenlosen KI-Tools kennen, die Sie direkt in Audacity einsetzen können. Diese werden Ihnen in Zukunft dabei helfen, Ihre Aufnahmen mit wenig Einarbeitungszeit zu optimieren und für die Veröffentlichung vorzubereiten.

Grundkenntnisse in Audacity sind von Vorteil, aber nicht zwingend erforderlich.

Anmeldung hier

Als virtuellen Lernort werden wir ZOOM nutzen. Der ZOOM-Link wird einen Tag vor Schulungsbeginn bis 13:00 Uhr versendet.

Are you passionate about exploring the intersection of digital technologies and the humanities? Join our PhD course ‘An Introduction to Digital Humanities and to the digital treatment of language data’ designed specifically for PhD students interested in applying digital methods to their humanities language data, and to thereby be able to answer new kinds of research questions on large amounts of such data. In this course, we will equip you with a basic understanding of various methods, standards, and tools essential for conducting digital humanities research on language data.
Our focus of the course will be on the digital processing of text and speech, exemplified among others through the extensive digital corpora available through The Department of Nordic Studies and Linguistics. You will have hands-on experience with the corpus tool Korp, enabling you to unlock valuable insights hidden within vast amounts of textual data. Additionally, we will look into natural language processing techniques, providing you with a practical introduction to zero-shot & few-shot prompting of large language models, and machine learning with Python. Finally, you will be introduced to modern prompting and RAG (Retrieval Augmented Generation) techniques for having large language models (LLMs) assist in annotating and analysing your material. You will get the opportunity to work with these tools also on your own data.
For more information about the PhD course, please contact the PhD Administration (phd@hrsc.ku.dk).

Institutions

• PhD School at the Faculty of Humanities at University of Copenhagen

We are calling in all AI enthusiasts in Hamburg. Let's talk and exchange thoughts and ideas!

Meet other AI and tech enthusiasts at our well-established AI Meetup, bringing together the Hamburg AI scene for more than three years. Join us for the AI Mix & Mingle at Sofabar, where you can chat casually, exchange ideas, or discuss your AI projects. There are no presentations—the focus is on networking and open conversation.

While your registration is not mandatory, it is appreciated. You would make our reporting easier if you sign up on Eventbrite. But feel free to drop by spontaneously, too, to connect with us and others, build networks, and dive into interesting conversations about AI and beyond!

We are lookimg forward to seeing you there!
For any questions, comments, or feedback, please email us at: cm@aric-hamburg.de
More information on our website: [https://aric-hamburg.de/veranstaltungen/ai-mix-mingle

Institutions

• Artificial Intelligence Center Hamburg (ARIC) e.V.

• UHH

In 30 Minuten erhalten Sie einen verständlichen Überblick über neuronale Netze und große Sprachmodelle (LLMs) – die heute dominierenden KI-Verfahren. Wir betrachten, wie Maschinen aus Daten lernen, wie einfache neuronale Netze und moderne große Sprachmodelle aufgebaut sind. Anschließend werden gesellschaftliche Chancen (z.B. Produktivität, Barrierefreiheit) und Risiken (z.B. Bias, Halluzinationen, Desinformation, Energieverbrauch, Blackbox) einander gegenübergestellt und mögliche Zukunftsszenarien vorgestellt. Für die Teilnahme werden keine technischen Vorkenntnisse vorausgesetzt.

Schwerpunkte sind:

• Grundlagen maschinellen Lernens
• Aufbau einfacher neuronaler Netze
• Funktionsweise moderner Sprachmodelle/generativer KI-Modelle
• Gesellschaftliche Chancen und Risiken
• Mögliche Zukunftsszenarien

Anmeldung hier

Als virtuellen Lernort werden wir ZOOM nutzen. Der ZOOM-Link wird einen Tag vor Schulungsbeginn bis 13:00 Uhr versendet.

This two-day workshop provides a structured introduction to single-cell RNA-sequencing (scRNA-seq) analysis, combining theoretical foundations with practical exercises to help participants understand and interpret scRNA-seq data. The theoretical components will present the complete conceptual workflow—from experimental design considerations to raw data processing, quality control, normalization, dimensionality reduction, clustering, and data integration. These sessions will guide attendees in evaluating scRNA-seq datasets, understanding the reasoning behind each part of the workflow, and selecting methods that lead to reliable biological insights.
The practical components translate these concepts into hands-on skills through guided exercises, primarily in Google Colab. Participants will become familiar with the Nextflow workflow manager, explore nf-core pipelines such as nf-core/scrna and nf-core/scdownstream, and learn to understand the structure and meaning of the results. They will also review data integration using the Semares platform and Cellxgene explorer, and perform custom downstream analyses in Scanpy. AI-assisted coding support will help attendees create their own analysis functions in Python and develop custom analysis, even with limited programming experience. The workshop will enable participants to complete an end-to-end analysis and prepare them to apply modern scRNA-seq methodologies to their own datasets.

Topics: here

This three-day online workshop provides an introduction to the validation of AI methods in image analysis. Reliable validation is essential to decide whether algorithms have a potential for real-world translation, beyond standard leaderboards. It ensures that model performance estimates are meaningful, reproducible, and reflecting the underlying research questions. The course introduces key principles and practical techniques for validating AI models.

Participants will learn how to select and interpret appropriate performance metrics (Day 1), quantify model performance uncertainty (Day 2), and assess the robustness of rankings in algorithm benchmarking (Day 3). The workshop combines theoretical lectures with guided hands-on exercises using provided datasets and Jupyter notebooks. We will use open tools such as Metrics Reloaded to map certain problems to suitable metrics, and Rankings Reloaded to assess ranking stability, along with new concepts to assess the probability of false outperformance claims in publications. In addition, short coding tasks will target the implementation and critical assessment of validation workflows.

More information & registration

• UHH

Computational Narratology beschreibt die Erforschung von Erzähltexten im Kontext der maschinellen Datenverarbeitung und konzentriert sich auf algorithmische Lesarten und Modellierungen narrativer Strukturen (frei nach Mani 2013). Dabei werden sowohl Phänomene der Textoberfläche (das Wie einer Erzählung) als auch Konzepte der Tiefenstruktur (das Was einer Erzählung) computationell untersucht. 

Vor diesem Hintergrund wird im Vortrag ein Promotionsprojekt vorgestellt, das sich mit Möglichkeiten der computationellen Analyse von Figurenmotivation in literarischen Texten beschäftigt. Auch wenn es bereits Vorschläge dazu im Bereich der Computerlinguistik und des Natural Language Processing gibt, stellt die Adaption dieser Verfahren für narrative Texte eine besondere Herausforderung dar.

Mani, Inderjeet. 2013. „Computational Narratology“. In the living handbook of narratology. Hamburg University Press. https://www-archiv.fdm.uni-hamburg.de/lhn/node/43.html.

Generative KI verändert die Hochschullehre rasant – von neuen didaktischen Möglichkeiten über rechtliche Fragen bis hin zu Herausforderungen für Prüfungen, Inklusion und Medienproduktion. Doch wie können Lehrende und Hochschulen diese Entwicklungen sinnvoll und verantwortungsvoll gestalten?

Die Veranstaltung bietet Raum für Austausch, Perspektivenvielfalt und praxisnahe Impulse. Bringen Sie Ihre Erfahrungen, Fragen und Ideen ein und gestalten Sie gemeinsam mit anderen Teilnehmenden die Zukunft der KI-gestützten Hochschullehre.

GPT und andere KI-Modelle lernen aus großen Textmengen und sind daher stark durch ihre Trainingsdaten geprägt. Darauf aufbauende Chatbots wie UHHGPT übernehmen Stereotype aus den zugrunde liegenden Modellen. In diesem 90-minütigen Workshop wird zunächst ein kurzer Blick hinter die Kulissen von KI-Modellen und Chatbots geworfen: Wie wird sprachliches Wissen in den Modellen kodiert? Wie entstehen sogenannte Halluzinationen? Was ist die RAG-Funktion, die über das Modell hinaus einen Echtzeitzugriff auf Informationen ermöglicht?

Im zweiten Teil können die Teilnehmenden verschiedene Prompting-Strategien selbst ausprobieren, zum Beispiel für Ideengenerierung, Recherche, Textarbeit oder Seminarplanung.

Wenn Sie keinen eigenen Laptop mitbringen können, geben Sie bitte vorab Bescheid, dann besorgen wir ein Leihgerät für Sie.

This workshop will focus on the analysis of spatial transcriptomics data. Participants will receive an introduction to spatial transcriptomics (particularly, the Visium and Xenium platforms from 10x Genomics) and a comprehensive hands-on session to understand the data analysis process. The workshop will cover key concepts, including data visualization, interpretation of gene expression patterns and spatial proximities, and clustering analyses.

Topics:

• Introduction to spatial transcriptomics platforms
• Methodological foundations
• Data visualisation
• Expression pattern interpretation
• Tutorial I – Preprocessing, implementation and evaluation metrics
• Tutorial II – Analysis, biological interpretation and discussion

Registration for this workshop will open soon.

Welche Auswirkungen haben das Aufeinandertreffen von traditioneller Internetsuche und generativer künstlicher Intelligenz auf das Informations-Ökosystem? Diese Frage steht im Zentrum des 16. Hamburger Mediensymposiums, veranstaltet vom HBI sowie der Medienanstalt Hamburg/Schleswig-Holstein (MA HSH) und der Handelskammer Hamburg.
Die traditionelle Suche und generative künstliche Intelligenz (KI) verschmelzen von beiden Seiten. Durch sogenanntes Grounding – unter anderem auf der Basis von Medieninhalten – beantworten KI-Bots inzwischen Fragen, die Bürger*innen auch an Medien richten. Gleichzeitig integrieren Suchmaschinen zunehmend KI-Elemente in ihre Funktionsweise und die Ergebnispräsentation. Die Auswirkungen auf das Informations-Ökosystem sind beträchtlich und könnten mediale Angebote potenziell gefährden. Wir müssen sowohl die Architektur der Technologien als auch die unterschiedlichen Informationsbedürfnisse der Bürger*innen, die an diese Angebote herangetragen werden, besser verstehen.
Im Rahmen der Veranstaltung möchten wir die aufgeheizte Debatte mit kommunikationswissenschaftlichen sowie medienrechtlichen, regulatorischen und medienökonomischen Einordnungen versorgen und diese aus unterschiedlichen Praxisperspektiven diskutieren.

Institutions

• HBI
• Medienanstalt Hamburg/Schleswig-Holstein
• Handelskammer Hamburg

Welche Auswirkungen haben das Aufeinandertreffen von traditioneller Internetsuche und generativer künstlicher Intelligenz auf das Informations-Ökosystem? Diese Frage steht im Zentrum des 16. Hamburger Mediensymposiums, veranstaltet vom HBI sowie der Medienanstalt Hamburg/Schleswig-Holstein (MA HSH) und der Handelskammer Hamburg.

Die traditionelle Suche und generative künstliche Intelligenz (KI) verschmelzen von beiden Seiten. Durch sogenanntes Grounding – unter anderem auf der Basis von Medieninhalten – beantworten KI-Bots inzwischen Fragen, die Bürger*innen auch an Medien richten. Gleichzeitig integrieren Suchmaschinen zunehmend KI-Elemente in ihre Funktionsweise und die Ergebnispräsentation. Die Auswirkungen auf das Informations-Ökosystem sind beträchtlich und könnten mediale Angebote potenziell gefährden. Wir müssen sowohl die Architektur der Technologien als auch die unterschiedlichen Informationsbedürfnisse der Bürger*innen, die an diese Angebote herangetragen werden, besser verstehen.

Im Rahmen der Veranstaltung möchten wir die aufgeheizte Debatte mit kommunikationswissenschaftlichen sowie medienrechtlichen, regulatorischen und medienökonomischen Einordnungen versorgen und diese aus unterschiedlichen Praxisperspektiven diskutieren.

Presentation: Dr. Melanie Andresen (DeepL)

Programmieren zu können eröffnet zahlreiche Möglichkeiten bei der Aufbereitung und Analyse von Daten und weit darüber hinaus. Aber wie schafft man den Einstieg ganz ohne Vorkenntnisse? In diesem Workshop probieren wir gemeinsam aus, wie man ein LLM als Lerncoach einsetzen kann.

Wenn Sie keinen eigenen Laptop mitbringen können, geben Sie bitte vorab Bescheid, dann besorgen wir ein Leihgerät für Sie.

The International Conference on Computational Science is an annual conference that brings together researchers and scientists from mathematics and computer science as basic computing disciplines, as well as from other various application areas, who are pioneering computational methods in sciences such as physics, chemistry, life sciences, and engineering, as well as in arts and humanitarian fields, to discuss problems and solutions in the area, identify new issues, and shape future directions for research.

ICCS 2026 in Hamburg, Germany will be the twenty-sixth edition in this series of highly successful conferences.

The conference theme for this year is “At the Forefront of Science through Computation and Data”.

This conference will be a unique event focusing on recent developments in: scalable scientific algorithms; advanced software tools; computational grids; advanced numerical methods; and novel application areas. These innovative novel models, algorithms and tools drive new science through efficient application in areas such as physical systems, computational and systems biology, environmental systems, finance, and others.

We are calling in all AI enthusiasts in Hamburg. Let's talk and exchange thoughts and ideas!

Meet other AI and tech enthusiasts at our well-established AI Meetup, bringing together the Hamburg AI scene for more than three years. Join us for the AI Mix & Mingle at Sofabar, where you can chat casually, exchange ideas, or discuss your AI projects. There are no presentations—the focus is on networking and open conversation.

While your registration is not mandatory, it is appreciated. You would make our reporting easier if you sign up on Eventbrite. But feel free to drop by spontaneously, too, to connect with us and others, build networks, and dive into interesting conversations about AI and beyond!

We are lookimg forward to seeing you there!
For any questions, comments, or feedback, please email us at: cm@aric-hamburg.de
More information on our website: [https://aric-hamburg.de/veranstaltungen/ai-mix-mingle

Institutions

• Artificial Intelligence Center Hamburg (ARIC) e.V.

• UHH

The Hamburg Network for Artificial Intelligence and Law (NAIL) will host a conference on “AI, Finance & Law: Innovation, Integrity, and the Future of Markets” on 2–3 July 2026 in Hamburg, Germany, and invites paper submissions.

This in-person conference will seek to explore a broad range of regulatory issues in relation to, inter alia, the following aspects:

• How should financial markets law respond to the adoption of AI systems by financial institutions and intermediaries? What are the economic effects of AI-driven financial markets, and are there risks to market integrity, efficiency, and to market stability?
• What governance mechanisms ensure accountability, transparency, and auditability of AI in financial services? How can market supervisors and investors effectively monitor the integrity of new AI-driven market players?
• How does the EU AI Act interact with existing and upcoming financial regulatory frameworks? How does the AI Act and associated rules apply to financial-markets use-cases such as algorithmic trading, risk models, and credit scoring?
• How can regulators balance innovation, competition, and financial stability in regulating AI? What forms of legal experimentation (sandboxing, testbeds, proportionality tools) support safe AI adoption and ensure innovation? 
• What are cross-border challenges with regulating AI across the EU and globally? What can the EU learn from global regulatory models such as the US, UK, Singapore, China, and international standard setters?
• Twin Transformation: How can advances in AI be balanced with sustainable development and green finance?

The conference organizers invite papers from scholars, policymakers, and practitioners on each of these topics, and on any other topic of relevance to the conference theme. In particular, the organisers encourage the submission of papers that examine these topics from an interdisciplinary, international and/or comparative perspective.

Institutions

• Network for Artificial Intelligence and Law (NAIL)

Data science (DS) and artificial intelligence (AI) have become central to modern Economics as the discipline has shifted from theory to data and computation. Today, economists work with massive, complex datasets and study increasingly nonlinear, strategic, and dynamic systems —- areas in which Data Science, AI and Machine Learning (ML) provide powerful tools for efficiently processing high-dimensional, unstructured, and large-scale data. Data Science and AI methods improve causal inference, prediction, and forecasting. They also inspire new approaches to modeling dynamic optimization and strategic behavior.

Conversely, Economics contributes foundational ideas that shape how AI systems are designed, evaluated, and deployed. Indeed, some of the most significant concepts in contemporary AI originate from economic theory, which provides the basis for decision-making (statistical decision theory), strategic reasoning (game theory), incentive design (mechanism design), causal inference (trustworthy AI), welfare analysis (AI ethics), and behavioral modeling (human-centered AI).

Institutions

• LMU Munich, Germany
• U of Bristol, UK
• EuADS Germany
• U of Essex, UK
• STATEC, Luxembourg
• U of Göttingen, Germany
• Data 3.0 Ldt., UK
• CUNEF Universidad, Spain
• U of Cordoba, Spain
• NeuralWave Technologies, Luxembourg
• Imperial College London, UK
• Bielefeld University, Germany

• UHH

This Dagstuhl Seminar seeks to ignite a focused dialogue on the strategic positioning and practical implementation of digital transformation in scientific research. Central to our discussion will be the role of Digital Centers or Hubs as flagship components of modern research infrastructure and the significance of Research Software Engineering (RSE) and Research Data Management (RDM) as essential enablers of their success.

The seminar's guiding question is: How can digital transformation be effectively embedded in the research landscape? Should it be steered centrally via comprehensive digital centers or emerge through decentralized, institution-driven initiatives? We will explore the strengths, limitations, and potential synergies of both models, with the goal of deriving actionable insights for sustainable digital infrastructure strategies.

Another focus is on the tension between disciplinary specificity and transdisciplinarity: Should digital competence centers closely align with specific research domains, or should they work across disciplines to foster collaboration and shared innovation? Understanding this balance is crucial for building effective, scalable digital ecosystems across institutions and research communities.

For more information please read here

Institutions

• Indiana University - Bloomington, US
• Universität Kiel, DE
• Universität Innsbruck, AT
• HCDS, Universität Hamburg, DE

We are excited to announce the Workshop on AI-Supported Research Software Engineering, an interactive event for research software engineers and scientists engaged in developing and maintaining research software in the era of AI.

Refer to this website for upcoming program details and registration.

Your orga team: Stephan Janosch, Inga Ulusoy, Mustafa Dogan, Bernadette Fritzsch, Bela Gipp, Lukas Netz, Sebastian Nielebock

Institutions

• Georg-August-Universität Göttingen