EVENTS

In dieser 30-minütigen Online-Schulung geben wir Ihnen einen Überblick darüber, wie Sie mithilfe von KI-Tools Ihren eigenen digitalen Zwilling - einen Avatar - erstellen und für Ihren videobasierten Unterricht nutzen können.

Die Gestaltung von Lehrvideos mit einem Avatar, der mit Hilfe von KI-Tools erstellt wurde, kann eine effektive Methode sein, um automatisiert ansprechende und personalisierte Lerninhalte zu erstellen.

Diese Online-Schulung ist ideal für Einsteiger/innen, die sich mit der Erstellung von digitalen Zwillingen vertraut machen möchten.

Die Stiftung Innovation in der Hochschullehre (StIL) und das Netzwerk Landeseinrichtungen für digitale Hochschullehre (NeL) stellen wegen des großen Bedarfs an KI-bezogenen Qualifizierungs- und Unterstützungsmaßnahmen vielfältige Angebote zur Verfügung.

Schwerpunkte sind:

• Was sind digitale Zwillinge und welche Anwendungen gibt es?
• Vorstellung der besten und zugänglichsten KI-Tools zur Erstellung von Avataren.

Anmeldung hier

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

Artificial Intelligence is transforming how we approach chemical research and synthesis. By teaching language models to understand and generate the language of chemistry, we have developed complementary AI systems that bridge the gap between computational design and experimental reality.

Our large language model system, ChemCrow, represents one of the first demonstrations of an AI system directly controlling robotic synthesis platforms, successfully executing the synthesis of compounds including organocatalysts and chromophores.

Complementing this, our small language model system, Saturn, currently the most sample-efficient molecular design algorithm, enables precise molecular generation with built-in synthesizability constraints. Saturn’s innovations include direct optimization against retrosynthetic predictions and integration of building block availability, ensuring that generated molecules are practically accessible.

Our work demonstrates how different scales of language models can work together to transform chemical research, from initial molecular design through to physical synthesis, potentially revolutionizing drug discovery, catalysis, and materials development.

The Machine Learning in Engineering (MLE) initiative integrates competencies and activities in the field of machine learning at Hamburg University of Technology (TUHH) and partner organizations from the Hamburg metropolitan area. Within the Machine Learning in Engineering (MLE) initiative, students, doctoral candidates, postdocs, researchers, and professors from all departments at the TUHH and from the partners are working interdisciplinary together. The aim is to conduct fundamental research particularly relevant for the development of new technology, thereby contributing to the digital transformation of the engineering sciences. In addition to basic research, the initiative aims at transferring knowledge to business and industry. Among a number of instruments for this kind of transfer, the annually organised MLE days provide a natural opportunity for education and knowledge exchange.

Registration: If you want to attend, please, send an email to Ulrike Schneider (in cc) with your name and affiliation. The registration is open until 01.03.2025. The number of participants is limited by the size of the lecture hall. For guaranteeing your participation and to make planning (particularly of the coffee breaks) easier, please register as soon as possible. Your data will only be used for organizing the MLE Days.

Program (preliminary):
09:00-15:00: Presentations by MLE-members and partners following the updated MLE structure here   
15:00-17:00: Poster & Networking session

Catering: There will be a coffee break in the morning and afternoon with hot drinks and snacks. Lunch in the canteen is to be paid for by the participants themselves.

Poster in English until 14.02.2025.

The organizing committee reserves the right to reject registrations and poster contributions. If you have any questions, please do not hesitate to contact the organizing team via mle@tuhh.de .

Institutions

• Hamburg University of Technology (TUHH) 

We are pleased to announce RL4AA'25, the third instalment of the workshop series organised by the Reinforcement Learning for Autonomous Accelerators (RL4AA) Collaboration. After two very successful workshops in 2023 and 2024 in Karlsruhe and Salzburg, we are excited to announce the next workshop for 2025. RL4AA'25 will be hosted by DESY in the beautiful port city of Hamburg, Germany.

The workshop will bring together experts from the fields of machine learning, accelerator physics, and high-performance computing to discuss the latest developments in the field of reinforcement learning for autonomous accelerators. The workshop will feature invited talks, contributed talks, and poster sessions, as well as a panel discussion on the future of autonomous accelerators.

RL4AA welcomes seasoned RL practitioners as well as newcomers. We are doing our best to make sure there is something for everyone, from introductory tutorials to advanced research talks.

Note also that this year’s workshop is organised in coordination with 5th ICFA Beam Dynamics Mini-Workshop on Machine Learning for Particle Accelerators (MaLAPA) workshop at CERN. Both workshops are organised such that you can attend them one after the other.

We are looking forward to welcoming you to Hamburg in 2025!

Learning to Optimize: How Machine Learning Drives Decisions at Companies like Uber

Have you ever wondered why and where taxi cars wait, or how smart Uber drivers make their decisions? This talk will delve into these intriguing questions. We will explore the concept of sequential decision-making and policy classes through a hands-on approach. Using taxi services as an example, we learn how service providers can optimize their decision-making. And guess what? There´s not as much magic behind it as you might think...so prepare to be surprised!

Distinguished Lecture Series in Business Analytics & Data Science

The Distinguished Lecture Series in Business Analytics & Data Science at Kühne Logistics University (KLU) presents public lectures on selected topics in analytics, data science, and their applications across various industries, including logistics and supply chain management. Targeted at students, alumni, industry professionals, and other interested individuals, the series offers insights into the latest advancements, trends, and challenges in these fields. Attendees gain valuable knowledge on how data-driven decision-making can transform business operations and enhance efficiency in today's digital economy.

On April 2nd, 2025, 2pm CEST, Mascha Schmidt, Victor Haguet and Fritz Niesel from the Applied Machine Learning Group based at the Jülich Supercomputing Centre will join us at the HiRSE Seminar Series with their talk about Bridging the Gap Between Private Data and AI: A Beginner’s Guide to Retrieval Augmented Generation (RAG).

Abstract:

AI is transforming administrative and scientific workflows by automating tedious cognitive tasks. However, one of our most valuable resources - private and sensitive documents - often remains inaccessible to AI solutions. Retrieval Augmented Generation (RAG) offers a way to bridge this gap. It enables AI models to efficiently process and search large private data sources while maintaining control over data access and security.
In this talk, we’ll guide you through the process of building a RAG system, from understanding its core components to integrating it into real-world applications. We’ll begin by introducing RAG and demonstrating how it enhances knowledge retrieval where standard LLMs fall short. Then, we’ll break down key concepts such as chunking, embedding, vector storage and retrieval. Finally, we’ll explore how to tailor RAG for advanced use cases and integrate it into data-driven applications.
Whether you’re a developer, researcher, or AI enthusiast, this session will provide a clear roadmap for using RAG effectively. Join us to learn when to use RAG, what to expect, and how to take your LLM applications to the next level.

Institutions

• Helmholtz Platform for Research Software Engineering - Preparatory Study

The National Ignition Facility (NIF) at Lawrence Livermore National Laboratory is a 2.2 MJ laser facility designed to achieve inertial confinement fusion ignition. In this approach, the 192 laser beams of NIF are used to compress and heat a millimeter-scale capsule of deuterium-tritium fuel to fusion ignition conditions where the fusion energy produced exceeds the laser energy delivered to the target. On Dec. 5, 2022, a NIF experiment crossed this ignition threshold for the first time. In the two years since, significant further progress has been made with recent experiments producing more than double the amount of laser energy used to drive the implosion.

This talk reviews the importance of the interplay of theory and high-performance computing with experiments, and critical hurdles that had to be overcome to reach ignition, recent progress in NIF experiments since ignition was achieved, and some of the current and future plans aiming for even higher fusion yields.

This work was performed under the auspices of the U.S. Department of Energy by Lawrence Livermore National Laboratory under Contract DE-AC52-07NA27344.

You can join the on-site colloquium or the Zoom meeting after registering here

The presentation series “Train your engineering network” (TYEN) on diverse topics of Machine Learning addresses all interested persons at TUHH, from MLE partners as well as from the Hamburg region in general and aims at promoting the exchange of information and knowledge between these persons as well as their networking in a relaxed atmosphere. Thereby, the machine learning activities within MLE, TUHH and in the wider environment shall be made more visible, cooperations shall be promoted and also interested students shall be given an insight.

Organisers:

• Mijail Guillemard
• Robert Kräuter
• Gregor Vonbun-Feldbauer
• Jens-Peter M. Zemke

The series will be continued in the summer semester 2025. If you are interested in presenting, please contact the organizers.
The lectures will be in English.

Future robots need to be robust and adaptable, and new design approaches are needed for new production methods. I will talk about my research in using evolutionary algorithms and biologically inspired methods with the aim of having more intelligent, robust, and adaptive behavior in robots. I will give a short introduction to some of the algorithms and show how we, at the University of Oslo, apply them in our research platforms for exploring automatic robot design and adaptation. Here, we take an embodied AI approach and aim to co-design the body and the behavior of the robots, such that they are well fit for their intended environments and tasks. These approaches are still at a fundamental research stage, and I will discuss potential future application areas, as well as challenges and opportunities related to the sustainability of these AI systems.

Learning objectives:

By the end of the session, participants will be able to:

• List some bio-inspired AI approaches for robot design and adaptation.
• Discuss challenges and opportunities related to algorithmic robot design.

Recommended mastery level:

• Some undergraduate level engineering or computer science skills would be beneficial, although not absolutely necessary.

In DaVincis Resolve gibt es verschiedene KI-Tools, die die Postproduktion erleichtern sollen.

In 30 Minuten erhalten Sie einen Überblick über den aktuellen Stand der Möglichkeiten, die es bereits gibt und die vielleicht bald hinzukommen. Wir schauen uns an, wie die Tools funktionieren und wie sie in den eigenen Workflow integriert werden können.

Grundkenntnisse in DaVinci Blackmagic Resolve sind nicht erforderlich, aber hilfreich.

Anmeldung hier

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

Wie sieht die Zukunft der Videoproduktion aus?

Diese Online-Schulung gibt Ihnen einen Überblick über die neuesten Anwendungen von künstlicher Intelligenz in der Videoproduktion.

Es wird gezeigt, wie KI-Tools die Art und Weise verändern, wie Videos erstellt, bearbeitet und optimiert werden.

Mittlerweile ist es sogar mit Hilfe von KI-Tools möglich, kurze Bewegtbildsequenzen aus Bildern und Text-to-Video-Prompts zu generieren.

Wie könnte nun ein zukünftiger Produktionsworkflow mit Hilfe von KI-Tools aussehen?

In dieser Schulung wird anhand von Beispielen gezeigt, wie generative KI die Videoproduktion verändert.

Anmeldung hier

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

The 2025 edition of the EuADS Summer School is dedicated to Automated Data Science (AutoDS) and will cover important branches of this research field in a tutorial style. With the increasing complexity of data science projects and the limited availability of human expertise, the idea of automating or partially automating the work of a data scientist has come to the fore in recent years. AutoDS aims to streamline the data science workflow, making processes such as data pre-processing, feature engineering, model selection, evaluation and deployment faster and more accessible. By reducing manual intervention, AutoDS enables both non-experts and data scientists to work more efficiently, scale projects, and make data science accessible to a broader audience. It leverages tools from automated machine learning (AutoML) frameworks, automated visualisation and interpretability techniques to enable efficient model tuning, robust evaluation and easy deployment. Despite its advantages in efficiency and scalability, challenges remain in automating subtasks that are context-dependent and require human interaction, as well as model interpretability, dependence on data quality, and ethical concerns related to bias in automated models. These and other issues will be addressed in a series of five tutorials delivered by leading experts in the field.

The Summer School emphasizes the interdisciplinary nature of data science and is primarily aimed at PhD students, postdoctoral and early-career researchers with a basic grounding in data science, statistics, machine learning, AI, or related fields, and an interest in interdisciplinary research and applications.

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

Welcome to the 2025 edition of the WarmWorld-ESiWACE3 Summer School, in the old town of Lauenburg near Hamburg!

The Summer School will give an insight into ICON , one of the state-of-the-art weather and climate science models. The students will learn basic meteorology concepts and will be invited to tackle code challenges using intermediate and advanced approaches from software engineering, high-performance computing and data analysis, all under the guidance of experienced lecturers from these various fields.

Important dates

• Apr 15, 2025 – Deadline for travel grants requests
• Apr 30, 2025 – Registration closes
• until May 15, 2025 – Notification of acceptance
• Jul 28 - Aug 7, 2025 – Summer School

Institutions

• IT Center for Science (CSC-IT)
• Deutsches Klimarechenzentrum (DKRZ)
• Deutscher Wetterdienst (DWD)
• Technical University of Munich (TUM)
• University of Cologne , Center for Earth System Observation and Computational Analysis (CESOC)
• Max Planck Institute for Meteorology (MPI-M) 

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 is the first call for participation on the 18th MT Marathon that will take place in Helsinki on August 25-29, 2025. The eighteenth edition of the MT Marathon will be organized by the Language Technology Research group at the University of Helsinki, Finland, with sponsorship of EAMT.

Each Machine Translation Marathon is a week-long gathering of machine translation researchers, developers, students and users featuring:

- MT Lectures and Labs covering the basics and tutorials.
- Keynote Talks from experienced researchers and practitioners.
- Presentations of research and open source tools related to MT.
- Hacking Projects to advance tools or research in one week or start new collaborations.

The registration registration is free of charge for EAMT members 

The programme is still under construction. 

Projects

We collect and share proposed projects before the Marathon, and the project topics are settled on the first day. Usually, most of the projects actually make it to the final presentation and some continue even (long) after the Marathon. More details will be added later.

Open Session

The MT Marathon will again host an open session with poster presentations related to MT/NLP research and open-source tools. We invite students, developers and researchers to submit short abstracts (1 page) featuring previously published results, open-source tool demos, and work in progress. Abstracts are lightly reviewed for topical scope, and all relevant submissions will be accepted for presentation.
Information about submission procedures will be announced later.

Institutions

• Technology Research group at the University of Helsinki 

The 6th preCICE Workshop will be held at the Helmut-Schmidt University of Hamburg on September 8-12, 2025. The workshop is a coming together of the preCICE community to share ideas, experiences and knowledge about using preCICE, and to learn from others in the process. Like always, we plan to have user and developer talks, hands-on training sessions, discussions with the developers about your applications and use cases, and plenty of opportunities for networking. Read more about how a preCICE workshop looks like.

The workshop will include a hands-on training course. The course is suited for both beginners and current preCICE users, since advanced topics will also be covered. We will extend the course by a new module on HPC.

In the developer talks, the maintainer team will present recent updates on dynamic meshes, mesh-particle coupling, and macro-micro coupling – to only mention a few highlights. And we will continue the standardization process of adapters and application cases, where you can help shaping the future.

Keep watching this space for updates on registration, the workshop program, and more.

Call for contributions
We are looking for talks and posters that could be beneficial for the wider preCICE community. Are you developing a new adapter (such as for G+Smo or ISSM last year)? Are you using preCICE for an exciting new application? Are you developing new methods that should not be missing from preCICE? If the answer to any of these questions was yes, we encourage you to submit a brief abstract for a 20 minutes talk. Are you revisiting one of the classical preCICE use cases? Did you already present your work in a previous workshop? Then we would be very happy to catch up with your work and we encourage you to present a poster. You do not need to submit a contribution to join this workshop. However, your contributions are very welcome!

CELLO brings together international experts in ocean turbulence, air-sea-ice interactions, and computational methods to model and better understand ocean dynamics. The meeting will take place from September 16th to 18th, 2025, at Bucerius Law School in Hamburg.

Nearly 50 years ago, the Max Planck Institute for Meteorology, under the leadership of Klaus Hasselmann, recognized the ocean's crucial role in the climate system. Since then, advances in computational power and observational techniques have dramatically enhanced our ability to observe and simulate dynamics that were previously beyond our reach. These advancements now offer deeper insights into Earth's climate and its potential future changes, including how, where, and to what extent these changes might occur.

CELLO seeks to convene leading scientists from our field to share the latest research findings, explore future directions, and foster opportunities for collaboration.

To learn more about the conference themes and convenors please refer to the Conference Program. Submissions and registration will open on this website in Spring 2025.

In recent years, the field of Machine Learning has made significant progress in theory and applications. This success is rooted in the mutual stimulation of mathematical insight and experimental studies. On the one hand, mathematics allows to conceptualize and formalize core problems within learning theory, leading, for instance, to performance bounds for learning algorithms. On the other hand, experimental studies confirm theoretical predictions and instigate new directions in theoretical research. This meeting aims to discuss the interaction between theory and practice, with focus on the current gaps between the two. The talks will be centered around themes including the following.

• Gradient Methods (gradient optimization, stochastic gradient, natural gradients, gradient applied to deep networks, ...),
• Natural Geometric Structures (Information Geometry, optimal transport geometry, ...),
• Generalisation Theory (statistical learning theory, complexity measures, Ill-posed inverse problems, regularization, implicit bias...)
• Functional analytical tools (approximation theory, harmonic analysis, ...)
• Overparametrization and random matrix theory (neural tangent kernel, lazy training, convergence of gradient descent, generalization bounds, ...)