Ammar Mirjan walks through a sprawling industrial hall in Birr in the canton of Aargau, where Brown, Boveri & Cie. once manufactured turbines. The huge cranes under the striking shed roof are not part of the typical atmosphere of a start-up. But the ETH spin-off MESH, which Mirjan founded together with Mattis Koh three years ago, needs space for technology development. The company, which has around 10 employees, has rented 800 square meters in the halls to automate work with reinforcing bars in the construction industry using robots.

Mirjan once completed an apprenticeship as an automation engineer at ABB before studying architecture and completing a doctorate at ETH Zurich with Gramazio Kohler Research. MESH’s technology is the result of several research projects, including that of the National Center of Competence in Research Digital Fabrication. The hall features strikingly curved walls made of reinforcing bars, some of which are filled with concrete or natural stone. A robotic arm can grip, bend, place and weld the individual bars independently. This makes it possible to construct shapes that would be far too time-consuming and complex to do by hand.

Reinforcement at the touch of a button

The core of MESH’s expertise lies in the software that processes the data and calculates the robot’s movements. It can generate the reinforcement cage of a free-form concrete wall at the touch of a button and send the data directly to the machine. The complexity of the shape is easy to handle thanks to the direct link between digital data and digital production. “Architects don’t normally have any connection to reinforcement,” says Mirjan. “Now they can design with it, as the construction directly influences the shape.”

The MESH technology was used, among other things, to automatically place the reinforcement for the 3D-printed Tor Alva in Mulegns. The flagship project pushes the boundaries of digital fabrication and demonstrates its new formal possibilities. MESH is also focusing on the freedom of form at the Architecture Biennale in Venice, together with Gramazio Kohler Research and the artist Armin Linke. In the Arsenale, the team is presenting an installation consisting of three floating rings made of reinforcing steel, in the middle of which a humanoid robot dreams of the future of digital construction. “How digitalization will change architecture is still an open question,” says research director Inés Ariza, who is responsible for the project.

For variety, against labour shortages

Beyond the Biennale, the reality of construction is still different. “The demand for complex shapes is limited, even if they can save material thanks to optimization,” says Mirjan. MESH has therefore expanded its business model. The spin-off also uses its robots to partially automate the use of conventional reinforcing bars. An automatic stirrup bender machine the size of a bus stands in a corner of the hall. The industry has been using the machine for some time to automatically bend and cut rebar. The robot now replaces the worker, who removes the differently shaped parts and stacks them carefully.

In this way, the startup is counteracting the shortage of labor, especially for such physically demanding jobs. Automation also helps to ensure that individual construction remains affordable. “No two buildings are identical, and the reinforcement of every staircase is slightly different,” says Mirjan. Digital solutions allow this diversity in construction to be maintained even as wages rise. The first MESH robots are being used by reinforcement manufacturers in Switzerland, to whom the spin-off rents out its technology.

Used in the Gotthard Road Tunnel

MESH also uses the robotic arm to identify reinforcing bars, control quality and improve logistics overall. The company has been awarded a major contract for the construction of the second Gotthard road tunnel. Robots knot the reinforcing bars for prefabricated concrete elements at the crossing points. With around 10,000 reinforcement cages, this requires mass production, in which automation can save time and money.

MESH’s technology can be used in a variety of ways, from free-form projects to standard prefabrication. The start-up will continue its research and try to open up new business areas. “Special projects such as the Tor Alva or at the Biennale are important to further develop the technology and communicate it to the construction industry,” says Mirjan. “Our innovations should find broad application and make a sustainable contribution to the industry.”

MESH at the Swissbau 2024. (Video: Girts Apskalns)

The article by Andres Herzog was originally published by ETH Zurich in June 2025.

From the first day, the excitement was clear. This was a debut not only for us as participants, but also for ETH Zurich Campus Heilbronn, whose team poured heart and precision into every detail. Their warmth and constant support made us feel welcome. From diverse keynotes and hands-on workshops to thoughtful activities and perfectly timed Lichtfest, the week felt thoughtfully crafted.

Each morning began with Smart Start Breakfasts, coffee, food, and short “thoughts to start the day” that nudged us to see AI as a paradigm shift in how we think. Throughout the week I learned from researchers at ETH Zürich and beyond, seeing how AI touches climate, health, language, and human–AI work. Talks from TU Berlin, TU Munich, UC Irvine, the University of Tennessee, and the Institute of Science Tokyo reshaped how I see AI’s role across fields. One lecture that truly stayed with me was Professor Torsten Hoefler’s public talk; it felt like time travel through the pivotal moments of modern technology. He argued about how we’ve moved beyond the Age of Data into the Age of Computation, and offered an angle I hadn’t considered before: that LLMs’ “hallucinations” might be seen not as flaws, but as sparks of intelligence and creativity.

Workshops with mentors from ETH Zurich, NASA, and Oak Ridge National Laboratory on foundation models turned hard ideas into tools I can use. Much of that learning happened in small teams; mine, XplorAI, included people from Kosova, Portugal, Ecuador, and China. We worked hard, laughed often, and proved that curiosity and good humor help as much as code.

On a very hot afternoon, likely the longest day of the year, we visited LVWO Weinsberg. Among the vineyards we heard how AI helps growers and cellars, from monitoring vines to tuning the process, subtly transforming even the most traditional of crafts. That mix of tradition and innovation mirrored Heilbronn itself, a city changing fast. All week we heard about Dieter Schwarz’s big support to turn the city into an AI innovation hub. It felt like watching a place build its future in real time. 

Perhaps my biggest takeaway was how deeply human this “ML&AI” week felt. We were a global mix of students, scientists, engineers, and thinkers, from all over the world united by open questions and a shared drive to explore them. I left with practical advice, clearer views of the research world, ideas that will stick, and friends. In my farewell note, I wished everyone a symbolic Dieter Schwarz to back their dreams. May readers of this piece find theirs, too.

What challenges arise at high-scale training that you couldn’t imagine, having only experienced lower scale? How can conditional diffusion be brought out to domains where being exact is more important than being creative? Why is it so important to consider uncertainty in modelling?

And more crucially: could Heilbronn, a city of 100,000 inhabitants in Germany that I’m almost sure you never heard of, become the next AI research and development centre of Europe?   

At this very moment, I’m the one taking the lead on this investigation, so let me briefly introduce myself.

Hi, I am Loric Herbé, a regular master student in Data science at ETH Zurich, and for the next few minutes, you will have the opportunity to step into my shoes and discover the backstage of an event that could very well be our most tangible piece of evidence.

Let us first draw some context, so that the investigation board gets clearer in your heads.

On this end of June 2025, a soberly titled “Interdisciplinary School on Machine Learning and Artificial Intelligence for Science” was hosted at the emerging ETH Zürich Campus in Heilbronn. Supported by a range of sponsors, it’s part of a broader effort by ETH to establish a serious research and innovation presence in the region. The goal of this event: gathering talents from around the globe, from master to industry, to deepen their intuition and understanding of the latest applications of machine learning in science. How? By alternating between theoretical lectures, talks, and hands-on sessions that polished their mastery, while it was still hot.

My role regarding this event? With a few other colleagues, we were tasked with coming up with hands-on material, under the shape of Jupyter Notebooks. A challenging experience, as we had to find the right balance between tackling High Performance Computing (HPC) problems, along with giving the right amount of theory and intuition, all the while fitting into time constrained blocks.

What I just described took place mostly prior to the event, which means that on-site, I almost became a regular participant, and this is under this prism that I’m willing to unveil with you the fantastic colours of this five-day-long life fragment.

It would be an absolute crime not to start by giving a glimpse of the talks that we have been given. Without going into details, these talks were pure gold from a Data Science student’s perspective. The amount of insights and creative solution tracks to common problems we face in the field was just surprisingly high, especially given the duration of the talks. And this applies to a very broad spectrum of interest, such as the double-edged sword that is given by constrained optimization, large scale training techniques effects on stochasticity, or even techniques to bring in model interpretability for fields that can’t accept treating a result as a black box’s answer, and this is only to name a few.

Among all these thought-provoking talks, I want to give a special shoutout to the one of Professor Andreas Krause, which gave an overview of the active learning and reinforcement learning fields, notably through the lenses of optimism in the face of uncertainty. The reason I’m mentioning this talk is that as a Data science student, the in-depth lecture “Probabilistic Artificial Intelligence” given by Professor Krause was probably one of the most valuable perspectives on Machine Learning that I was ever given, and I’ll even go the extent that this also changed some of my perception and modeling assumptions on my life in general.

On another note, I can’t forget to mention what it felt like to have such brilliant minds buzzing around you for a week. And there I’m referring both to the organisers as well as the participants. To give my words some concrete pictures, imagine that in the first 10 minutes sitting in the bus to Heilbronn, I was discovering the current state of the art quantization technique for LLMs at inference time through some talk with another master student whose thesis built around that, and that my first meal revolved around learning a new perspective on model validation through Posterior Agreement with the eminent Professor Joachim Buhmann.  However, don’t get me wrong, although this could be a brain racking experience at some times, this also retained the social development aspect that you could find in a regular summer camp, where you have good laughs around a nice diner and talk about the near future under a nice sunset.

Taking back half-seriously my investigator costume, I would say that it’s hard to answer whether Heilbronn will truly become tomorrow’s AI centre of Europe, but it’s worth pointing out that it’s through such efforts, organising such events, that it will definitely earn some key position in this fast-paced field. 

What a journey we made together! Now, I believe that the most acute readers will feel that there is a missing piece to conclude this investigation. And they would be right, what happened to the first few questions? Sadly, I don’t yet pretend to be able to answer those questions at the same level those eminent professors did. However, I think I might have a solution… can’t wait to walk, alongside you this time, in a future second edition!

This summer, check out some the interesting and inspiring journeys of ETH Zurich students who stepped outside the classroom and into innovation ecosystems around the world through ETH Studios.

From collaborating with Bloomberg in New York to exploring machine learning in the Swiss Alps, investigating extraterrestrial life with NASA, and reflecting on Europe’s tech future, these student-authored stories offer a unique glimpse into how ETH talent is shaping global conversations.

Here’s your curated summer reading list, straight from the ETH Ambassadors Blog:

1. SQL, LLMs, Bagels and Skyscrapers – A Journey of 5 ETH Students in NYC

By Maša Nešić, Maurice Hanisch, Eric Nothum, Vinícius Mohr, and Carlo Teufel

What happens when five ETH students step into the heart of the financial world for a 3-day tech challenge at Bloomberg’s New York office? A lot of learning, a lot of inspiration and just enough time for a bagel break. In their own words, “We meticulously worked on the project, exploring our team’s dynamics and attempting to put everyone’s skills to best use.”

Read the story

2. Bridging Innovation and Inspiration – TECH 2025 Heilbronn

By Giancarla Salzarulo

As part of the ETH delegation to TECH 2025 in Heilbronn, Giancarla reflects on the power of global dialogue, the challenges of scaling innovation in Europe, and why being part of ETH means more than just studying. She emphasizes, “I arrived with curiosity and left with inspiration. Being part of ETH Zurich’s story continues to be one of the greatest privileges of my life.”

Read the story

3. Mayhem in New York

By Noè Canevascini

What started as a spontaneous interview turned into a full-blown mentorship adventure in New York City. Noè’s story captures the unpredictability and opportunity that ETH Studio experiences can spark. He says, “Despite the challenges, this experience reinforced my love for tackling new opportunities and mentoring others.”

Read the story

4. Machine Learning Meets Geology: My Internship at SLF Davos

By Paulina Körner

At ETH Studio Davos, Paulina blended AI with earth sciences to explore how computer vision can detect borehole fractures, a step toward smarter, safer geotechnical work. She shares, “One of the highlights of my internship was the people I had the chance to work with. The team at SLF was made up of researchers and professionals from all over the world, each bringing a unique perspective to their work.”

Read the story

5. Learning About Extraterrestrial Life Detection at NASA JPL

By Marco Trentini and Adrian Fuhrer

How do you detect life on a moon of Saturn? During their seven-month stay at the NASA Jet Propulsion Laboratory, Adrian and Marco focused on developing instruments that can be used to detect life forms on other planets, particularly Enceladus, a moon of Saturn. Their work in the Chemical Analysis and Life Detection Lab aimed to understand and utilize the chemical processes involved in detecting life on other planets!

Read the story

ETH Studios

ETH Studios are global innovation outposts where ETH students tackle real-world challenges alongside companies, researchers, and startups. Whether in New York, Heilbronn, Davos, or at the experimental StudioX, students gain entrepreneurial and interdisciplinary experience while contributing to sustainable and tech-driven solutions.

Contact Francesca Caggiula at eth-studios@ethz.ch if you are interested in more information on and about the ETH Studios.

Happy reading!