EVENTS

The Global Initiative on AI for Food Systems (GI-AI4FS) brings together leading international organizations, International Telecommunication Union (ITU) , Food and Agriculture Organization of the United Nations (FAO), World Food Programme (WFP), and International Fund for Agricultural Development (IFAD) alongside a diverse global community of experts and innovators to shape the future of food through artificial intelligence. This open, collaborative platform invites participation from across disciplines, including AI/ML, digital agriculture, food systems science, and related fields.

The first Meeting of GI-AI4FS will be hosted in Rome by FAO on 18 June 2026. Participation is free, welcoming experts, institutions, and stakeholders committed to transforming food systems through responsible and impactful AI.

Institutions

• AI for Good

Oft fällt Gestaltung erst auf, wenn sie Probleme verursacht, etwa wenn Informationen nicht verständlich sind, Materialien überladen wirken oder alltägliche Abläufe ins Stocken geraten. In dieser Schulung zeigen wir, wie grundlegende Designprinzipien dazu beitragen, Kommunikation, Lehrmaterialien und Prozesse klarer, zugänglicher und wirksamer zu gestalten. Wir sprechen außerdem über Sehgewohnheiten, klassische Fallstricke und nützliche Tools.

Für die Teilnahme an dieser Online-Schulung werden keine Vorkenntnisse vorausgesetzt. 

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

Der Workshop von 3S – Solution Support Service bietet dir eine praktische Einführung in den Schutz vor Cyberangriffen für kleine und mittelständische Unternehmen (KMU).

Was dich erwartet:

• Einblick in die Bedrohungslage für KMU
• Schwerpunktthema: KI als Schwachstelle
• Live-Hacking erleben – wie Angreifer vorgehen
• Verständliche Erklärungen zu IT-Sicherheit und richtigen Reaktionen
• Tipps zu E-Mail-, Download- und Web-Management
• Bedeutung und Funktion eines Backups
• Praxisnahe Alltagstipps, um Ihr Unternehmen zu schützen

Mit dem schlagartigen Aufkommen von KI-Generatoren für unterschiedlichste Medienformen seit Ende 2022 stehen Hochschulen hinsichtlich ihrer Lehre und ihrer digitalen Prüfungsumsetzung enormen Möglichkeiten aber auch Herausforderungen gegenüber. Neben Fragen des Urheberrechts und des Datenschutzes ist insbesondere der Bereich des Prüfungsrechts von diesen Entwicklungen betroffen und es bedarf geeigneter Maßnahmen für einen konstruktiven Umgang mit dieser technologischen Revolution.
Dabei stellt sich vor allem auch die Frage: Können per KI-Generator erstellte Texte und Medieninhalte überhaupt als künstlich generierte Inhalte erkannt und detektiert werden? Und wenn ja, wie verlässlich sind die Ergebnisse von KI-Detektoren und wie lassen sich diese im Prüfungsrecht berücksichtigen? Auch ergibt sich bei solch rasanten Technologieentwicklungen immer die Notwendigkeit einer staatlichen Regulierung, wie sie im Zuge des EU AI-Act beabsichtigt wird. Welche Implikationen bringt diese Regulierung allgemein und speziell für den Hochschulbereich mit sich?

Schwerpunkte sind:

• KI-VO-E bzw. AI-Act (Entwurf)
• Hochschulgesetze (der Länder, insbesondere Hamburg)
• Studien- und Prüfungsordnungen

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

Kittel, Code, VR-Brille: wie intelligent muss KI sein, um in der ärztlichen Ausbildung zu unterstützen?«
mit Prof. Benjamin Risse

Institutions

• MSH Medical School Hamburg

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.

Sebastian Müller from Universität Potsdam will join us at the HiRSE Seminar Series to talk about “When you don’t know what to test for: Metamorphic Testing for Research Software”

Abstract: As research software becomes increasingly complex and interdisciplinary, assuring its quality and keeping the software well maintained requires testing approaches that go beyond conventional correctness checking. Traditional unit testing techniques are often insuffiecient in research software contexts where expected outcomes may be uncertain, stochastic, or influenced by complex simulation contexts. Not being able to predict what the correct output of a program should be for a given input is known in the literature as the oracle problem.
This talk presents practical strategies for tackling the oracle problem within standard automated testing frameworks, such as pyunit. Rather than abandoning unit tests, the focus is on rethinking the assertions they rely on. One complementary approach will be discussed in depth: Metamorphic Testing. The technique will be illustrated with real-world, scientific software examples.
The seminar is aimed at RSEs and developers of research software with a basic knowledge of automated testing who want to strengthen their testing knowledge with practices suited for hard-to-test research software protoypes.

Institutions

• Helmholtz Platform for Research Software Engineering - Preparatory Study

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.

The Hub of Computing and Data Science (HCDS) warmly invites researchers, RSEs, and developers to come together and:

• connect with peers across disciplines and institutions
• discover real-world RSE applications in science
• share ideas, tools, and experiences
• gain visibility for their work

Call for Posters

We invite you to showcase your work in our poster session — a great opportunity to present your projects, get feedback, and spark new collaborations.

All poster presenters can also give a 3-minute lightning talk to introduce their work to the audience.

We welcome contributions from all disciplines and experience levels.

What to expect

• Inspiring keynotes on successful RSE practices
• Interactive barcamp sessions – bring your own topics
• Poster session & lightning talks
• Plenty of time for discussion

In dieser 30-minütigen Online-Schulung erfahren Sie, wie künstliche Intelligenz die Musikproduktion revolutioniert. Sie erhalten eine grundlegende Einführung in die Möglichkeiten von KI-Tools für die Musikproduktion und Soundgenerierung.

Wir stellen Ihnen die gängigsten und benutzerfreundlichsten KI-Tools zur Musik- und Soundgenerierung vor, damit Sie selbst kreativ werden können – ohne musikalische Vorkenntnisse!

Diese Schulung ist speziell für Einsteiger*innen konzipiert, die wenig bis keine Erfahrung mit Musikproduktion oder KI haben.

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

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.

Instructor: Dr. Lucia Testa, Institute of Medical Systems Bioinformatics, UKE

The aim of the course is to provide an introduction to  graph-based tools. Many real-world systems, including biological networks, molecular structures, and social interactions, are naturally represented as graphs. The workshop will introduce the fundamental concepts behind learning on graphs with a focus on graph theory, graph signal processing and graph neural networks. Participants will learn the mathematical foundations of graph-based learning and gain practical experience implementing models using PyTorch and PyTorch geometric. The course will combine theoretical explanations with hands-on tutorials. The workshop will be in presence and each participant should bring their own laptop.

Topics:

• Introduction to graph-structured data
• Fundamentals of graph signal processing
• Message passing and Graph Neural Networks
• Graph-level learning
• Implementing Graph Neural Networks
• Training and evaluating models on real datasets

Registration for this workshop is now open!

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.

Institutions

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

• UHH

Menschzentrierte KI-Implementierung in der Praxis: Einblicke aus dem BSP-Drittmittelprojekt Mittelstand-Digital Zentrum Zukunftskultur«
mit Kristina Bodrozic-Brnic

Institutions

• MSH Medical School Hamburg

In Adobe Premiere Pro gibt es mittlerweile verschiedene KI-Tools, die die Postproduktion unterstützen und erleichtern sollen. In 30 Minuten geben wir einen Überblick über den aktuellen Stand der Möglichkeiten, die es bereits gibt und die vielleicht bald hinzukommen.

Wir werfen einen Blick darauf, wie die KI-Tools funktionieren und wie sie in den eigenen Workflow integriert werden können. 
Grundkenntnisse in Adobe Premiere Pro sind nicht erforderlich, aber hilfreich.

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

Künstliche Intelligenz (KI) ist eine der zentralen Technologien unserer Zeit. Um den interdisziplinären Austausch und die Sichtbarkeit der KI-Forschung zu stärken, lädt das KI-Institut der Helmut-Schmidt-Universität / Universität der Bundeswehr (HSU) zu einem regelmäßigen KI-Forum ein – eine Plattform für spannende Keynotes, Einblicke in aktuelle Forschungsprojekte und inspirierende Vernetzung.

WAS ERWARTET SIE?

• Keynote von führenden Expert:innen aus Industrie oder Wissenschaft zu aktuellen Themen der Künstlichen Intelligenz
• Kurzpitches von Promovierenden mit innovativen Forschungsprojekten
• Postersession & Get-Together, um mit Forschenden, Entwickelnden und Interessierten in den Austausch zu treten

FÜR WEN IST DAS KI-FORUM?

• Forschende und Lehrende mit Interesse an KI
• Unternehmen & Start-ups im KI-Bereich
• Entwickler:innen und Technologie-Expert:innen
• Alle, die sich mit KI vernetzen und inspirieren lassen möchten

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

Prof. Dr. Torsten Mütze, Universität Kassel

Institution

• UHH

AI for Good is unlocking AI’s potential to serve humanity

AI for Good is organized by ITU in partnership with over 50 UN Sister Agencies and co-convened with the Government of Switzerland.

The inaugural Global Dialogue on AI Governance, established under the recent landmark UN General Assembly Resolution A/79/L.118 on AI, and hosted by the UN Secretary-General with ITU’s support, will be held back-to-back along the margins of the 2026 AI for Good Global Summit.

• UHH

Aditi Punetha from RWTH Aachen University will join us at the HiRSE Seminar Series to talk about “Exploring the Lifecycle of Research Software: Insights from a Literature Review and Empirical Study”

Abstract: Modern science relies on research software to enable data analysis, simulation, and experimentation across domains. Yet, research environments are dynamic and entail funding constraints, posing challenges for the stability and maintenance of research software. This thesis explores the lifecycle of research software with the help of a semi-structured literature review and an empirical study. 
The literature review examines traditional software development lifecycle models, such as the waterfall and spiral models. It offers a comparison with frameworks tailored specifically for research contexts, such as the research software maturity model and FAIR4RS principles. The literature review showcases that traditional software development lifecycles offer beneficial structural organisation and quality assurance principles, but their linear approach and inflexibility complicate their applicability to the research environment.
With the aim of gathering practical insights, an empirical survey was conducted among 66 participants actively engaged in research software development. The exploratory survey examined ubiquitous development practices, challenges, and the mindset of developers towards lifecycle management, the sustainability of research software, and the transferability of industrial practices to research contexts. 
Results indicate that development processes in research contexts are often informal and heterogeneous, as documentation, long-term sustainability, and reproducibility emerge as major concerns. Participants, however, also appreciated the potential benefits of a hybrid approach that incorporated the rigid structure of traditional software development lifecycles with the flexibility required in research settings.
The study reveals that the diversity and complexity of research software development defy adequate representation by a single overarching model. Rather than rigid models, adaptive and context-sensitive frameworks integrating attributes of long-term maintenance, community engagement, and openness are of vital importance for ensuring quality, reproducibility, and longevity in research software engineering.

Institutions

• Helmholtz Platform for Research Software Engineering - Preparatory Study

Prof. Dr. Linda Kleist, Universität Hamburg, ALGO

TBA

Institution

• 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

This 7-hours course covers typical mathematical tasks and caveats of image reconstruction problems. We introduce the theoretical framework of inverse problems and how different imaging modalities and measurement errors can lead to unfavorable reconstructions. Furthermore, we consider regularization strategies to overcome these issues, both from the theoretical and practical side. This fundamental knowledge is to be used as a starting point for self-guided learning during and beyond the course time. Basing off of the classical regularization approaches, the course further gives an introduction into deep learning for inverse problems. Finally, we also explore the Bayesian viewpoint, where we also consider the problem of uncertainty quantification.

The workshop covers a lecture and a tutorial part with hands-on, during which the instructors are available for instructions, feedback and advice.

NOTE: Registration will open August 19, 2026, 12 pm.

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