The Cyber Valley Start-up Network is expanding, admitting three new members: Feyer, Ontic Labs, and oneAI. These start-ups are using bold ideas and groundbreaking technologies to shape the future of AI and robotics in Baden-Württemberg.
Feyer is building a European frontier AI lab for automated physical invention. The team develops AI systems designed to explore complex physical design spaces and create new technological solutions, with potential applications across areas such as photonics, precision instrumentation, and quantum technologies.
The company emerged from the research group of Mario Krenn, Professor at the Cluster of Excellence “Machine Learning: New Perspectives for Science” at the University of Tübingen. The founding team consists of Jonathan Klimesch, Sören Arlt, and Mario Krenn and has already reached an exciting milestone: Feyer was selected as one of ten teams for the first stage of SPRIND’s Next Frontier AI Challenge, together with Ontic Labs.
Ontic Labs is building a foundation model for physical intelligence, giving machines a working understanding of how the physical world behaves.
The Tübingen-based startup develops physics-aware world models that enable machines and robots to perceive, predict, and act in complex real-world environments. Its approach brings states, actions, and sensor data into a shared representation, allowing physical properties such as contact, forces, and object coherence to emerge from training rather than being manually programmed. The goal: a single world model that can transfer across robots, embodiments, tasks, and environments.
Founded by Omid Taheri, Mikel Zhobro, Georg Martius, Wieland Brendel, and Gerard Pons-Moll, Ontic Labs brings together expertise from the Max Planck Institute for Intelligent Systems, the University of Tübingen, the Tübingen AI Center, and the ELLIS Institute Tübingen. The team was also selected as one of ten teams in SPRIND’s Next Frontier AI Challenge.
SPRIND’s Next Frontier AI Challenge
The two start-ups from Tübingen are among just ten teams across Europe that have been selected and will receive €3 million to develop technologies beyond today’s AI models. Of the three selected teams from Germany, two originate from the Cyber Valley ecosystem in Baden-Württemberg as spin-outs from several of Cyber Valley’s academic partners, including the ELLIS Institute Tübingen, Max Planck Institute for Intelligent Systems, Tübingen AI Center, and the University of Tübingen. Their success highlights the growing momentum with which AI research is being translated into innovative companies and how excellent AI research from Baden-Württemberg is playing a key role in the European race for frontier AI models.
As AI adoption accelerates across organizations, questions around governance, security, compliance, and control are becoming increasingly important. oneAI is addressing exactly this challenge: enabling companies and public organizations to use AI securely, transparently, and in compliance with regulatory requirements.
The start-up is a spin-out from relyon AG, already part of the Cyber Valley Corporate Network, demonstrating Baden-Württemberg’s powerful research-to-innovation pipeline. Mirko Tochtermann, Founder & CTO of oneAI, shared his perspective on secure AI adoption and governance at one of our latest Cyber Valley IMPULSE events.
The research-to-market journeys of these start-ups highlight how transferring academic expertise into application leads to tangible impact, powering industry and driving Europe’s sustainable future. Cyber Valley is delighted to welcome these three start-ups to its community!
The Cyber Valley Start-up Network
The Cyber Valley Start-up Network brings together more than 100 innovative AI and robotics start-ups across Baden-Württemberg, forming a strong and growing entrepreneurial community within one of Europe’s leading AI and robotics ecosystems. The network spans a broad range of applications and industries, from foundational AI technologies to applied and industry-specific solutions. Connected through their close ties to Cyber Valley, members benefit from access to world-class research, industry partners, investors, and public stakeholders, jointly strengthening the region’s AI and robotics ecosystem.
The Tübingen AI Center invites applications for two tenured full professorships (W3) in Machine Learning and Intelligent Systems, to start as soon as possible. The successful candidates will be appointed at the University of Tübingen. The positions provide generous, permanent core funding (e.g. for PhD students or postdocs) and substantially reduced teaching responsibilities.
They are a federal competence center for AI education and research. Their research pushes the boundaries in numerous aspects of intelligence, including algorithms and models for machine learning, autonomous, causal, and agentic intelligence, as well as interaction with humans, markets, and society at large. Their core mission is to educate and empower the next generation of researchers to build the technological foundations of a competitive, sustainable, sovereign European society – through excellence in teaching, active technology transfer and societal action. They invite applications in all areas of machine learning, artificial intelligence, and related disciplines.
They seek internationally renowned candidates who pursue compelling long-term research agendas that complement and extend their expertise. Your application should explicitly articulate how you intend to contribute to their mission. They offer competitive budgets and compensation packages, with permanent funds allocated for research staff.
Candidates will be evaluated based on their potential for further growth in scientific excellence, considering past scientific contributions as well as the novelty, creativity, and uniqueness of their research vision.
Required qualifications include a PhD or equivalent degree with exceptional expertise in machine learning as well as postdoctoral qualifications and teaching experience equivalent to the requirements of a full professorship. Teaching and research are conducted in English.
The University of Tübingen is committed to equity and diversity and actively promotes equal opportunities. Female academics, in particular, are explicitly invited to apply, as are candidates from outside Germany. Applications from equally qualified candidates with disabilities will be given preference.
For more information on professorships, the hiring process, and the German academic system, please click here.
Location: The center is located near Stuttgart in the historic university town of Tübingen, a beautiful location in southwest Germany close to a large natural preserve. Major cities such as Zurich, Munich and Frankfurt can be reached within a few hours by public transport. Stuttgart airport, with many direct flights to major international cities, is just half an hour away.
Training deep-learning models requires large amounts of computing power, time, and energy. It also often depends on carefully chosen hyperparameters, such as the learning rate, which determine how a neural network learns.
Led by Frank Schneider at the University of Tübingen, this project developed more autonomous and resource-efficient training methods. The goal was to enable training algorithms to adjust important hyperparameters automatically, reducing the need for manual tuning. The project ran from 2022 to 2024.
Key results
Developed training methods that can adapt hyperparameters during the training process.
Investigated techniques for reducing the computational demands and energy consumption of deep-learning training.
Advanced second-order and curvature-based methods, which use information about the shape of the optimization problem to improve training.
Established AlgoPerf: Training Algorithms, a community benchmark for reliably comparing how quickly different training algorithms reach a given level of model performance.
Developed AlgoPerf through an open collaboration involving more than 20 researchers from academia and industry. The initiative was co-chaired by George Dahl of Google DeepMind and Frank Schneider of the University of Tübingen.
Made the algorithms, benchmark results, and research findings publicly available so that academic and industrial laboratories can reproduce and build on them.
Why it matters
Deep learning is often limited by the cost of training large neural networks. More autonomous training algorithms could reduce the need for expert intervention while lowering computational costs and energy consumption.
AlgoPerf is particularly important because it provides a common evaluation standard: training methods can be compared according to how efficiently they achieve a specified level of performance, rather than only by their final results. This may accelerate progress toward more efficient and accessible deep learning.
This project produced the following peer-reviewed publications:
Tatzel, L., Hennig, P., & Schneider, F. (2022). Late-Phase Second-Order Training. NeurIPS 2022 Workshop: Has it Trained Yet?. [open access]
Eschenhagen, R., Immer, A., Turner, R., Schneider, F., & Hennig, P. (2023). Kronecker-Factored Approximate Curvature for Modern Neural Network Architectures. Advances in Neural Information Processing Systems 36 (NeurIPS). [open access]
Kasimbeg, P., Schneider, F., Eschenhagen, R., Bae, J. et al. (2025). Accelerating neural network training: An analysis of the AlgoPerf competition. International Conference on Learning Representations (ICLR). [open access]
Fernandez, A., Schneider, F., Mahsereci, M., & Hennig, P. (2025) Connecting Parameter Magnitudes and Hessian Eigenspaces at Scale using Sketched Methods. Transactions on Machine Learning Research (TMLR). [open access]
Many machine-learning systems can learn patterns from images or sensor data, but they often struggle when decisions must follow strict rules. Real-world tasks may involve budgets, capacities, safety limits, or actions that are required or forbidden.
Led by Georg Martius, this project investigated how neural networks can be combined with combinatorial optimization—mathematical methods for choosing the best option from many discrete possibilities. The project developed ways to embed optimization algorithms into learning systems, allowing AI to learn from data while making decisions that respect predefined constraints. It ran from 2021 to 2024.
Key results
Developed methods for integrating integer-programming and other combinatorial optimization problems into neural networks.
Created neuro-algorithmic policies that combine learned representations with structured algorithms, improving generalization to new combinatorial problems.
Developed methods for planning directly from visual input, connecting perception from images with decision-making in environments involving difficult search problems.
Introduced techniques for training neural networks through discrete optimization procedures, even when these procedures are not naturally differentiable.
Developed LPGD, a general framework for backpropagation through embedded optimization layers, making it easier to train systems that contain classical solvers.
Demonstrated how optimization components can serve as trainable building blocks rather than being treated as separate tools.
Why it matters
The project provides a foundation for AI systems that connect perception with structured, constraint-aware decision-making. Such systems could help robots plan actions safely, enable logistics systems to allocate limited resources, and support applications in scheduling, planning, and operations management.
The approach does not replace classical optimization solvers. Instead, it integrates them into larger learning pipelines so that neural networks can interpret data while established algorithms handle rules and constraints. This combination could make AI systems more reliable and adaptable, although their performance still depends on how accurately the relevant constraints and optimization problem are modelled.
This project produced the following peer-reviewed publications:
Anselm Paulus, Michal Rolínek, Vít Musil, Brandon Amos, and Georg Martius. “CombOptNet: Fit the Right NP-Hard Problem by Learning Integer Programming Constraints”. In: Proceedings of the 38th International Conference on Machine Learning. Vol. 139. Proceedings of Machine Learning Research. PMLR, July 2021, pp. 8443–8453. url: https://proceedings.mlr.press/v139/paulus21a.html.
Marin Vlastelica, Michal Rolinek, and Georg Martius. “Neuro-algorithmic Policies Enable Fast Combinatorial Generalization”. In: Proceedings of the 2021 International Conference on Machine Learning (ICML). July 2021. url: https://proceedings.mlr.press/v139/vlastelica21a.html.
Marco Bagatella, Mirek Olšák, Michal Rolínek, and Georg Martius. “Planning from Pixels in Environments with Combinatorially Hard Search Spaces”. In: Advances in Information Processing Systems (NeurIPS 2021). 2021. url: https://openreview.net/forum?id=XgGUUaKgips.
Subham Sahoo, Anselm Paulus, Marin Vlastelica, Vít Musil, Volodymyr Kuleshov, and Georg Martius. “Backpropagation through Combinatorial Algorithms: Identity with Projection Works”. In: Proceedings of the Eleventh International Conference on Learning Representations. May 2023. url: https://openreview.net/forum?id=JZMR727O29.
A. Paulus, G. Martius, and V. Musil. “LPGD: A General Framework for Backpropagation through Embedded Optimization Layers”. In: Proceedings of the 41st International Conference on Machine Learning (ICML). Vol. 235. Proceedings of Machine Learning Research. PMLR, July 2024, pp. 39989–40014. url: https://proceedings.mlr.press/v235/paulus24a.html.
Human movement depends on complex interactions between the brain, nervous system, muscles, and body. The brain does not calculate the exact force required for every muscle and joint; instead, the body’s mechanical properties and neural reflexes help coordinate movement automatically.
Led by Daniel Häufle, Georg Martius, and Pierre Schumacher, this project investigated whether morphological computation could help AI systems learn human-like movement more efficiently. Morphological computation refers to the way the body itself contributes to movement through its structure, materials, and passive mechanical responses.
The project was carried out at the University of Tübingen between 2021 and 2024.
Key results
Developed learning methods for controlling computer models with many muscles and degrees of freedom.
Showed that large musculoskeletal systems are difficult to train, but that carefully guided learning can produce stable, human-like movements.
Generated bipedal walking patterns that remained stable on uneven terrain.
Demonstrated that simple muscle-like properties can make learned movements more natural and robust.
Combined reinforcement learning with biologically plausible objectives, rather than training AI systems only to minimise mathematical error.
Transferred a walking pattern learned in simulation to a real robot without additional training, demonstrating a promising approach to sim-to-real transfer.
Why it matters
The project shows how the physical structure of a body can contribute to intelligent movement and reduce the burden on a control system. This could help researchers build robots that move more efficiently, adapt more naturally to disturbances, and interact more safely with their surroundings. The findings may support future applications in healthcare, rehabilitation, prosthetics, and advanced manufacturing.
This project produced the following peer-reviewed publications:
In 2016, important actors from science, industry, and politics founded the Cyber Valley Consortium, which became the first Innovation Campus in Baden-Württemberg. Ten years on in 2026, we’re revisiting the most important milestones from the last decade. Each month, we’ll focus on a particular year since Cyber Valley’s beginning.
This month, we’re looking back to 2018, which marked an important moment for the exchange between AI research and society in Tübingen. From the end of November to the beginning of December, activists occupied a lecture hall in the Kupferbau (a university lecture hall in Tübingen) to demonstrate their concerns with Cyber Valley. They drew attention to the Innovation Campus’s collaboration with private companies and demanded greater public involvement in decision-making and transparency about its research.
The lecture hall occupation demonstrated the public’s interest in AI and the importance of open conversations about its impact on society. To increase dialog between researchers and the public, Cyber Valley launched several initiatives, including the Cyber Valley Public Advisory Board, the Cyber and the City exhibition at the Tübingen Stadtmuseum, the KI-Bürgerrat, as well as numerous public engagement activities from Cyber Valley GmbH.
Engaging the public in AI research: Philipp Hennig, Professor for the Methods of Machine Learning at the University of Tübingen
While the protests came as a surprise to many members of the Cyber Valley Community, they highlighted the importance of public engagement when building Baden-Württemberg’s future with AI. To ensure that the whole of society benefits from Cyber Valley’s research and collaboration with industry, the public needed to be involved in decision-making, as Philipp Hennig explains:
“On a late morning in 2018, I showed up for my lecture in the Kupferbau to find the building occupied. I walked up to the protest camp, interested to see what good cause the students were fighting for. It took a few bewildered moments before I realized that their banners had something to do with me. But I believe the protesters may also not have realized that there was a lecture in the building by and for the very people they were protesting against. At least, they seemed unprepared when I asked them to join me in the lecture hall so we could talk in front of my students.
Over the weeks that followed, I understood this mutual surprise was a metaphor for the situation in town. Students and the wider public did not know or understand what was happening on our nascent AI campus on the hill. And how could they? We had never made an effort to tell them. In our defense, it had not occurred to us that anyone would want to know. AI was still an emerging topic then. Many of us academics, myself included, thought of our work as a nerdy niche.
December 2018 and January 2019 would prove to be a very stressful time. But in hindsight, I am grateful for the experience. Many of my colleagues, from PhD students to professors, stepped up to speak publicly, often in antagonistic settings, about their work, and the often misunderstood administrative details of research funding. Perhaps the most interesting effect of the protests was what it did to Computer Science students. Most of them were initially indifferent. But as they found themselves forced to side with either their fellow students from other faculties or their own professors, many of them became increasingly engaged.
I learned that winter that explaining oneself—in public, and in person—is painful, but worth it. Seven years later, Tübingen may be more harmonious. But the question to our students has only become more pertinent: What kind of world do they want to build with AI?”
Photo: Friedhelm Albrecht/Universität Tübingen
Founding the Cyber Valley Public Advisory Board: Regina Ammicht Quinn, Senior Professor at the International Center for Ethics in the Sciences and Humanities (IZEW) at the University of Tübingen
One of the outcomes from the protests was the establishment of the Cyber Valley Public Advisory Board (PAB)—an independent committee that critically questions the ethical and social implications of the research projects from the Cyber Valley Innovation Campus. Here, PAB spokesperson, Regina Ammicht Quinn, recounts her experiences of the protests and how they led to the PAB’s founding:
“I experienced the occupation of the Kupferbau as remarkably civilized and cooperative. There were notes everywhere about what was allowed and what was not, chess was played in the afternoon, and the atmosphere was generally characterized by politeness and mutual respect. For example, when I wanted to hold an event in the Kupferbau as part of my lecture series on the topic of “Discrimination by Algorithms”, the sleeping bags were unceremoniously put aside so that the lecture could take place. In my opinion, the protesters did not formulate any concrete demands. Rather, they articulated general concerns and fears about the social consequences of artificial intelligence—questions and uncertainties that, in my view, have lost none of their relevance to this day.
The occupation’s aim was to draw attention to problems: next to the main demand ‘Amazon not in Tübingen!’, came calls for greater transparency—especially regarding possible weapons research—and fears of the already difficult housing situation deteriorating further. There were no concrete problems that were easy for the university to solve. The students articulated general concerns and fears regarding AI’s social consequences—questions and uncertainties that, in my view, have lost none of their relevance to this day. Universities are also committed to society, so a lecture hall can sometimes be a place of protest.
The establishment of a Public Advisory Board in Cyber Valley was therefore a necessary and important step. The Public Advisory Board should create the space in which social perspectives, ethical questions, and public concerns about AI research are brought into discussion. The board’s initial responsibilities included reviewing project proposals and making recommendations. It did not expressly see itself as a moral control body. Rather, it saw its role in pointing out that digital research should be more interdisciplinary and that additional scientific perspectives are necessary to broaden horizons.”
Cyber and the City: Thomas Thiemeyer, Professor for empirical cultural studies at the University of Tübingen
The protests also gave an impetus to cultural researchers like Thomas Thiemeyer, who curated the Cyber and the City exhibition at the Tübingen Stadtmuseum. The exhibition ran from February 2023 to January 2024 and gave an overview of the developments and debates surrounding AI research in Tübingen. For Thiemeyer, it offered a space for the public to engage with this topic following the protests in 2018:
“The protests raised some of the big questions related to AI that were little discussed publicly in Germany at the time: Where will our city develop if the tech industry and AI research become central players? What does AI do to our society? How does it change our everyday lives? And which economic models (keyword: ‘surveillance capitalism’) do we support with new technologies? What does this mean for scientific research, whose independence from commercial interests and political expectations is important?
The protests were the reason why we, as cultural researchers, dealt with the topic and finally made an exhibition on it. The protests meant that the topic of AI in Tübingen and Stuttgart became a topic for the people of Tübingen, where fundamental questions of urban and social development were negotiated. Protests are always interesting for cultural studies because they force societies to reflect upon themselves and make corrections.
A continuing dialog with the public about AI research and application is important because new technologies always change people’s lives. These technologies affect all of us and are therefore a subject of political discussion. For these discussions to be conducted not only by a few experts, but by as many citizens as possible, information is a prerequisite.”
The events of 2018 highlighted the importance of open discussions about new technology. AI developments not only raise scientific and technological questions but also touch on societal expectations, hopes, and concerns. This is why it is crucial to create spaces for the exchange of different perspectives.
Public engagement is an important part of Cyber Valley’s activities. Various public engagement formats and initiatives promote continuous exchange between researchers and the public—from dialog events and exhibitions to participatory formats such as citizens’ dialogues and campus tours. The aim is to make AI understandable, include different perspectives, and consider the social dimensions of new technologies.
To celebrate Cyber Valley’s ten-year anniversary, we’ll be sharing a variety of content across our website and social media channels reflecting on milestones from the last decade. Stay tuned for the next installments!
Pierre Schumacher has been selected as one of the two recipients of the “RIG Outstanding Doctoral Thesis Award”, recognizing exceptional doctoral research in robotics in Germany. The award was officially announced during the “RIG Heroes Award Night 2026” at the German Robotics Conference 2026, organized by the Robotics Institute Germany (RIG), in Cologne on March 12.
Schumacher’s dissertation, “Reinforcement Learning for Muscle-Driven Systems,” was selected from 27 nominations submitted by 16 institutions across Germany. Following an extensive evaluation process, an international selection committee chose two equal awardees, citing Schumacher’s work for its “scientific depth, originality, and impact”.
Pierre Schumacher conducted his doctoral research at the Max Planck Institute for Intelligent Systems in Tübingen under the supervision of Prof. Georg Martius (University of Tübingen) and Prof. Daniel Häufle (Hertie Institute for Clinical Brain Research) funded by the Cyber Valley Research grant “Learning effcient control of non-linear muscle-driven systems: Morphological computation as guiding principle”. His project was part of the Cluster of Excellence “Machine Learning: New Perspectives for Science” and the Center for Bionic Intelligence Tübingen Stuttgart (BITS). His dissertation, “Reinforcement Learning for Muscle-Driven Systems,” addresses challenges in controlling complex, highly overactuated robotic and biomechanical systems by developing new machine learning methods.
A central contribution of the work is the development of DEP-RL, a reinforcement learning approach that improves exploration and learning in systems with many redundant actuators, such as musculoskeletal models. Using this method, Schumacher demonstrated efficient learning for tasks involving complex simulated bodies, including a human model with dozens of muscles and animal-inspired locomotion systems.
Pierre’s research also explored how muscle-like actuation can improve learning performance and robustness in robotic systems. In collaborative studies, Schumacher and colleagues showed that musculoskeletal models can lead to more data-efficient and stable learning in anthropomorphic control tasks. More recent work demonstrated that reinforcement learning can produce natural and robust bipedal walking in musculoskeletal simulations without relying on motion-capture demonstrations.
Schumacher began his academic path with a trinational bachelor’s degree in physics that took him to universities in France, Luxembourg, and Germany. After completing his B.Sc. in physics in Saarbrücken 2017, he shifted toward computational approaches and pursued a master’s degree at Goethe University Frankfurt in Frankfurt am Main. In 2021, he joined the Max Planck Institute for Intelligent Systems and the Hertie Institute for Clinical Brain Research in Tübingen with Prof. Georg Martius and Prof. Daniel Häufle for his PhD in Computer Science, which he completed in 2025.Schumacher’s expertise has also attracted industry attention: he joined MyoLab, a startup based in New York focusing on musculoskeletal control technologies, last year.
The RIG Outstanding Doctoral Thesis Award highlights emerging researchers whose doctoral work significantly advances robotics research in Germany. Schumacher’s selection underscores the growing importance of combining machine learning, biomechanics, and embodied intelligence to enable the next generation of adaptive robotic systems.
In 2016, important actors from science, industry, and politics founded the Cyber Valley Consortium, which became the first Innovation Campus in Baden-Württemberg. Ten years on in 2026, we’re revisiting the most important milestones from the last decade. Each month, we’ll focus on a particular year since Cyber Valley’s beginning.
This month, we’re returning to 2016 to look back at Cyber Valley’s foundation. Back then, the AI boom and the wholesale use of LLMs were still some years away. Actors from science, industry, and politics in Baden-Württemberg nevertheless knew that this technology would play an essential role in the state’s future.
Baden-Württemberg was already in an unrivalled position to create AI for the benefit of future society since it is home to several top AI research institutions: the Max Planck Institute for Intelligent Systems, the University of Stuttgart, the University of Tübingen, Fraunhofer-Gesellschaft, and Karlsruhe Institute of Technology (KIT). The next step was to apply their cutting-edge research to solving real-world problems.
This was where Cyber Valley came in. In 2016, the Cyber Valley Consortium was founded with the mission of advocating for the research, development, application, and acceptance of AI and modern robotics. Cyber Valley was to unite excellent research with practical application to build the AI of tomorrow in Baden-Württemberg.
At its founding, Cyber Valley Consortium consisted of the Max Planck Society (which includes the Max Planck Institute for Intelligent Systems, MPI-IS), the state of Baden-Württemberg, and the Universities of Stuttgart and Tübingen, alongside partners from industry: Amazon, BMW, Bosch, IAV, Mercedes-Benz Group, Porsche AG, and ZF Friedrichshafen. Other research institutions joined later, including Fraunhofer-Gesellschaft (in 2019), KIT, and ELLIS Institute Tübingen (both 2024).
Winfried Kretschmann, Minister-President of Baden-Württemberg, Theresia Bauer, who was then Minister of Science, Research and Art in Baden-Württemberg, and Martin Stratmann, who was then President of the Max Planck Society, launched the consortium on December 15, 2016, in Stuttgart’s Neues Schloss along with other participants from the consortium, including Bernhard Schölkopf(MPI-IS), Frank Allgöwer (University of Stuttgart) and Bernd Engler (University of Tübingen). The result was one of the largest research–industry partnerships for AI and modern robotics in Europe.
Photo: MPI-IS / U. Regenscheit
Theresia Bauer, Managing Director of the Baden-Württemberg-Stiftung, and Baden-Württemberg’s Minister of Science, Research and Art at the time of Cyber Valley’s founding:
“At the time, I was thrilled by the great opportunity to bundle the excellent research landscape in the fields of AI and computer science in a condensed innovation ecosystem and make it more attractive for top international talent. When Cyber Valley was founded, I hoped that this concentration of cutting-edge research, the promotion of young talent, and responsible innovation would have what it takes to develop great appeal and grow rapidly. Today, you can see that we have succeeded in developing such a positive dynamic, which has a far-reaching impact on science, business, and the entire location.”
Bernhard Schölkopf, Scientific Director of the ELLIS Institute Tübingen and Director of Empirical Inference at MPI-IS:
“We started Cyber Valley with the belief that AI has the potential to change the world, and with the goal to do so in a positive way. This is now commonplace, but launching in 2016 gave us a head start and with a number of exciting start-ups, outstanding research institutions, and initiatives such as ELLIS and MP-AIX, we now have one of the most vibrant AI ecosystems in Europe.”
Michael Black, Director of Perceiving Systems at MPI-IS:
“The goal of Cyber Valley has always been to give Germany the chance to build a new high-growth economy around AI. In 2016, AI was not in the headlines, but we believed that we had the raw materials needed to succeed. Through the partnership of government, academia, and industry, we grew the AI faculty, attracted top researchers, built a top graduate program, trained hundreds of AI scientists, attracted investment, and helped launch a network of start-ups. These start-ups were always the goal and they hold the promise of a new Germany economy.”
Photo: MPI-IS / U. Regenscheit
Peter Middendorf, Rector of the University of Stuttgart:
“From the very beginning, the University of Stuttgart pursued the goal of pooling expertise in artificial intelligence and particularly in robotics, within and for the Stuttgart region. Together with the University of Tübingen, the Max Planck Institute for Intelligent Systems, and other partners, we have played a decisive role in shaping and advancing Cyber Valley. This strong network has given rise to excellent research, new professorships, and innovative projects, which we continue to actively drive forward – soon also in the new Cyber Valley building here in Stuttgart.”
Philipp Hennig, Director of Tübingen AI Center, University of Tübingen:
“The world was a different place ten years ago, but even then it was clear that Europe, Germany, and Baden-Württemberg had to increase their efforts to stay relevant for AI. A decade later, I’m glad that we got the political mandate to get started then. The urgency of our tasks has only grown since then; but so have our opportunities.”
Bernhard Beckert, Dean of the KIT Department of Informatics:
“When Cyber Valley was founded in 2016, we saw its potential to increase the visibility of AI research in Baden-Württemberg and to build a regional network that connects talent and accelerates scientific progress. Today, more than ever, this collaborative approach remains essential for supporting responsible AI development and for strengthening the region’s position within an increasingly complex international environment.”
Matthias Tröndle, Head of the Scientific Coordination Office, MPI-IS:
“When we founded Cyber Valley in 2016, we envisioned creating a bold innovation ecosystem focused on artificial intelligence at a time when its transformative impact was not yet widely recognized. By bringing together universities, non-university research institutions, industry, start-ups, foundations, and government in an unprecedented partnership, we broke new ground. The outstanding success of Cyber Valley has since made it a blueprint for subsequent innovation campuses.”
To celebrate Cyber Valley’s ten-year anniversary, we’ll be sharing a variety of content across our website and social media channels reflecting on milestones from the last decade. Stay tuned for the next instalments!
