Frontier AI systems have recently solved IMO problems, discovered new mathematical constructions, and resolved open Erdős problems. Yet a more mundane question remains: 🤔how should a working mathematician actually use these tools day to day?
In this talk, titled “The Agentic Researcher: Turning AI Coding Agents into Research Assistants” Emil Partow, PhD Student of our relAI Director Prof. Gitta Kutyniok presented a recent paper from Prof. Pokutta (Zuse-Institut Berlin) that offers a concrete answer. The authors propose a five-level taxonomy of AI integration into research, ranging from classical work without AI to fully autonomous research loops. They have implemented this idea within an open-source framework.
Following a detailed presentation by Emil Partow, members of relAI and Prof. Kutyniok's research group gathered to discuss this forward-looking topic. The presentation explained the open-source tool that implements core research "commandments" (such as preventing the falsification of experimental data) via a practical, actionable loop. Participants then discussed how AI agents are already shaping research methodologies, what is required to implement these workflows successfully, and how to ensure human oversight remains at the center of the process. Emil also shared a practical case study demonstrating the tool in action, sparking a broader reflection on the evolving role of AI in modern research.
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Machine learning models, such as ChatGPT and those used in autonomous driving, are becoming essential tools in our daily lives. However, the existence of Adversarial Examples demonstrates that these systems are not free from vulnerabilities. To ensure their reliability, it is crucial to proactively address the potential risks associated with their use in critical safety applications.
In a recent blog post, relAI PhD student Lukas Gosch introduces the concept of Adversarial Examples and discusses Certifiable Robustness, a methodology designed to combat 🛡️ them.
What is an Adversarial Example?
As Lukas Gosch outlines, Adversarial Examples are deliberately crafted inputs that cause machine learning models to misclassify data. For example, the strategic placement of stickers on traffic signs can lead to incorrect identification of road signs by machine learning systems used in autonomous vehicles. Additionally, if an adversary manipulates the training data upon which these models are built, this too qualifies as an Adversarial Example.
How to combat Adversarial Examples?
In his post, Lukas describes Certifiable Robustness, a methodology for verifying the resilience of machine learning systems against adversarial examples, and explores the challenges associated with it.
🎤 Michael Lachner, CEO of Aqarios and one of the first relAI alumni, is developing solutions that combine quantum computing, AI, and advanced optimization algorithms to solve complex challenges in industry and business. In his interview with DAAD, he talks about his vision for the future of quantum computing in industry and Germany’s role as a leader in this transformation.
The interview in a 📸 snapshot:
🔹 Companies should start preparing today to leverage quantum computing and avoid missing the next major technological leap.
🔹 Germany has strong potential to play a leading role in the global AI and quantum landscape – driven by top-tier research, growing investment, and innovative start-ups.
🔹 Lachner values the academic excellence and networking opportunities provided by relAI and Zuse Schools across Germany.
As our current Coordinator is taking an exciting 👏 next step in their academic career to transition into a professorship 🎓, a wonderful opportunity has opened to join our team.
If you are interested in working at our innovative and interdisciplinary school, where you can help shape the research and education of the next generation of experts in reliable AI, we encourage you to check out the job posting.
If you meet the qualifications and would like to apply, please submit your application by June 14, 2026.
Coordinator of the Konrad Zuse School of Excellence in Reliable Artificial Intelligence (m/f/d)
The Konrad Zuse School of Excellence in Reliable Artificial Intelligence (relAI), founded in 2022, is one of three DAAD funded AI schools in Germany. relAI is a joint endeavor of TUM and LMU and has quickly become an international lighthouse for education and research in reliable AI in Germany. More than 100 MSc and doctoral researchers participate simultaneously, supervised by more than 50 professors. Our network includes international AI centers, non-university research organizations and various industrial partners. relAI opens up career paths in academia and industry to talented young researchers from around the world. The coordination office of this graduate school is located at and embedded in the infrastructure of the Munich Data Science Institute (MDSI) – an Integrative Research Institute at the Technical University of Munich (TUM) with an interdisciplinary and cross-faculty focus in the field of data science, machine learning and A
As the Coordinator of relAI you will be responsible for managing school-wide and extra-school activities.
Key responsibilities:
Developing and organizing events and training courses, e. g. career fairs, seminars, symposia, workshops, seasonal schools, retreats and conferences
Reporting to the directors, the DAAD and various stakeholders
Serving as the primary point of contact for our fellows and for our academic and industrial partners
Developing and executing recruitment strategies for the school´s candidates
Overall responsibility for the school's budget monitoring and financial reporting in coordination with TUM’s central administration; overseeing the allocation of funds and maintaining the comprehensive records and databases of the relAI network
Implementation and maintenance of efficient project management structures to ensure smooth daily operations of the graduate program.
Coordination of the application for project continuation after 2027.
Your qualification:
We are looking for a reliable and collaborative person able to manage a broad range of tasks and responsibilities with
Postgraduate degree (MSc or equivalent), combined with a certain affinity for data science, machine learning and AI
Professional experience in project management and/or science management, ideally in coordinating university programs or graduate schools
Leadership skills and a good intercultural understanding, high motivation, flexibility and reliability
Fluent in German and English, written and spoken, negotiating skills
Beyond standard MS Office, experience in managing digital collaboration tools and technical infrastructures is highly desirable (e.g., mailing lists, basic SQL databases, or content management systems for website maintenance).
We offer:
Full responsibility from the start in an innovative environment and with real impact on the data science ecosystem at TUM and in Munich
International, attractive, and interdisciplinary working environment across different departments and disciplines
Salary according to TV-L (depending on qualification up to E13) including social benefits.
Application:
The position can start immediately, and the contract duration is initially limited until December 31, 2027; a further extension is envisioned, subject to continued project funding. As an equal opportunity and affirmative action employer, TUM explicitly encourages nominations of and applications from women as well as from all others who would bring additional diversity dimensions to the university’s research and teaching strategies. Preference will be given to disabled candidates with essentially the same qualifications. If you are interested, please apply until June 14, 2026 with a cover letter (stating also your earliest date of entry), CV and relevant certificates plus reference letters via email to application@mdsi.tum.de (please attach a single PDF file and use the subject Coordinator relAI).
As part of your application for a position at the Technical University of Munich (TUM), you are transmitting personal data. Please note our data protection information in accordance to Art.13 General Data Protection Regulation (GDPR) for the collection and processing of personal data in the context of your application (https://portal.mytum.de/kompass/datenschutz/Bewerbung/). By submitting your application, you confirm that you have taken note of TUM´s data protection information.
Contact: https://zuseschoolrelai.de/ Email: coordinators@zuseschoolrelai.de Technische Universität München Munich Data Science Institute Konrad Zuse School of Excellence in Reliable AI Walther-von-Dyck-Straße 10 85748 Garching bei München
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🔮How will artificial intelligence (AI) influence the future of learning?
Ethics training in the field of Artificial Intelligence (AI) is essential. AI is revolutionizing our world and transforming various aspects of society, including education, politics, and economics. Researchers in AI are facilitating these changes by developing the conceptual and technical foundation 🧱 for new social structures.
🤔 However, are AI researchers fully aware of their responsibility and the impact of their work on future society?
To encourage reflection on these issues and provide foundational ethics training, relAI recently introduced an Ethics course as a mandatory part of the curriculum. The course is taught by Prof. Ruth Müller from the Department of Science, Technology, and Society at the TUM School of Social Sciences and Technology at Technische Universität München. The first edition took place last winter semester and was well received by relAI students.
One of these students, Valentine Idakwo, has written an insightful blog post to share the lessons learned from the course. This engaging lecture encourages students of AI to analyze their work from an external perspective, considering the social context in which their work is embedded.
📢 We are excited to announce that the call for applications to the MSc program 2026 of our Konrad Zuse School of Excellence in Reliable AI (relAI) is now open!
The innovative relAI MSc program is an addition to the MSc program at TUM or LMU, offering a cross-sectional training for successful education in AI. It provides a coherent, yet flexible and personalized training by enhancing scientific knowledge, professional development courses, and industrial exposure.
Funded applicants will receive a scholarship of up to 992 EUR (depending on independent income). They are further supported by travel grants, e.g., for home travel.
We highly encourage you to apply if you have:
an excellent bachelor’s degree in computer science, mathematics, engineering, natural sciences or other data science/machine learning/AI related disciplines;
Please help us in spreading the word, especially to excellent international candidates.
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Are you interested in contributing research for underserved communities? Don't miss the EEAMO Conference 2026, the 6th ACM Conference on Equity and Access in Algorithms, Mechanisms, and Optimization.
This event will showcase work across the research-to-practice pipeline, aiming to ensure that algorithmic systems serve a broadly beneficial role in society by advancing equity and expanding access to opportunities for underserved communities.
relAI research will be featured at the International Conference on Learning Representations (ICLR), which will take place this year at the Riocentro Convention and Event Center in Rio de Janeiro, Brazil, from April 23rd to 27th, 2026. ICLR is one of the leading conferences with significant impact and reputation in machine learning and artificial intelligence research.
relAI Publications at ICLR
Seventeen publications from relAI will be presented at the conference, fifteen of them in the main track.
We are happy to announce that Barbara Plankhas joined relAI as a Fellow!
Barbara is Full Professor for AI and Computational Linguistics at LMU Munich, where she holds the Chair in AI & Computational Linguistics and co-directs the Center for Information and Language Processing (CIS). She also serves as Head of the Munich AI & NLP lab (MaiNLP) and visiting Professorship at the IT University of Copenhagen
Her research on robustness, domain shift, and human label variation aligns well with relAI’s Algorithmic Decision Making research area. This work explores how AI systems learn and make decisions in the face of uncertainty and disagreement. Additionally, her emphasis on interpretability, reasoning, and trustworthy evaluation provides essential foundations for developing reliable, fair, and transparent algorithmic decision systems.
At relAI, Barbara will contribute by participating in seminars, workshops, and panels, as well as offering career advice.
