Studies

Living buildings, quantum computers, safety by design, home robots and AI-assisted pandemic preparedness. Europe’s researchers are reshaping how we live, work and design our cities in 2026

As science continues to push boundaries, the coming years could surprise us with self‑healing cities, robots that care and smarter defences against future pandemics. Here are five outlooks from some of Europe’s leading researchers.

Bio‑architecture – a return to nature

Imagine a city where buildings are alive – structures that absorb pollution as people walk by and adapt through growth. Architect Phil Ayres believes this vision is within reach.

He explains how recent advances in biohybrid architecture, from fungus‑based materials to climbing plants, are opening possibilities for sustainable design and reshaping our urban environments.

According to Ayres, a professor at the Royal Danish Academy in Copenhagen, our cities have largely been designed to serve a single species: humans. His work in biohybrid architecture points toward a future where other living organisms play an active role in the built environment, helping reconnect people with the natural world.

He has explored how organisms such as fungi (in the Fungateria and FUNGAR projects) and climbing plants (the flora robotica project) might function as architectural materials.

“Traditional construction materials are usually mined, transported and processed at high temperatures before becoming durable building components,” he explains. “We’re investigating how living complexes could be leveraged as part of a building’s fabric.”

While fungal materials are not yet strong enough to replace concrete or steel, Ayres notes that buildings rely on many materials beyond these two.

If we begin to grow parts of our buildings – much like we grow trees – we could gain environmental benefits such as carbon sequestration and enhanced biodiversity. This approach could also extend beyond buildings to other forms of urban infrastructure.

Anyone walking through a modern city can see how small patches of greenery are overshadowed by vast expanses of concrete and steel.

Biohybrid architecture could also draw on forestry and agricultural waste, as well as by‑products from food and industrial processes, supporting a more circular economy. Living materials might even provide additional functions, such as filtering air or water, or repairing themselves when damaged.

As new materials emerge, we may need to rethink supply chains, construction methods and even the aesthetic possibilities of living, growing structures – ultimately creating spaces that reconnect us with the outdoors.

Ayres acknowledges that the construction industry is cautious and slow to change; we have built in much the same way for a century. But research into living materials is advancing quickly.

Though these materials cannot yet serve as primary structural elements, future versions may offer the strength and durability needed to support entire buildings.

Quantum computing edges closer

Quantum computing is moving steadily from the laboratory into everyday life. Italian electronics engineer Giulia Acconcia explains how European researchers are shifting from theory to practice – with major implications for data security and battery innovation.

More companies in Europe are engaging with quantum technologies, a sign that quantum computers are edging closer to real‑world use, says Professor Giulia Acconcia of the Polytechnic University of Milan. She believes powerful quantum machines will soon tackle problems that today’s supercomputers cannot.

“In the past decade we’ve seen real progress, but developments have accelerated in the last five years,” she says. “Quantum computers are poised to leave research labs and start influencing people’s lives.”

In the EU‑funded QLASS project, her team is building a quantum computer using photons – tiny packets of light that travel faster than electrons and can encode more information.

“This lets us increase the amount of information transmitted inside the glass waveguides of a quantum computer,” she explains. A photonic chip, she adds, looks like a miniature network of glass roads that photons race along.

One of the researchers’ key goals is to use quantum computing to optimise battery design – a complex challenge involving many variables. Better optimisation could shorten electric vehicle charging times and enable cars to travel farther on smaller batteries.

Future users won’t need to operate quantum computers directly. Much like storing photos in the cloud, people will access quantum machines remotely and request complex calculations.

“These problems are so demanding that classical computers simply can’t solve them within a reasonable time,” Acconcia says.

Hormone‑disrupting chemicals lurk everywhere

Chemicals found in everyday products can quietly disrupt our bodies over time. Dutch toxicologist Majorie van Duursen, who studies risks to women’s health, explains how better regulation – and smarter personal choices – could mitigate long‑term harm.

Many chemicals can interfere with hormones and cause lasting health effects, warns van Duursen of the Vrije Universiteit Amsterdam. Her research in the EU‑funded FREIA initiative examined endocrine‑disrupting chemicals and their links to breast cancer, infertility, pregnancy complications, early menopause and endometriosis.

“We are gaining more insight into early life exposure to these chemicals. It’s not always that ‘the dose makes the poison’, because it can matter more when you’re exposed, even to very low doses. Hormones shape your body’s blueprint and changing them can have long‑lasting effects.”

Ongoing research is revealing the full extent of the problem, with studies showing that hormone disruption by chemicals leads to long‑term health issues, including conditions not recognised before, such as heart disease.

While it is impossible to avoid all chemicals, van Duursen stresses that individuals can still reduce their exposure. “Don’t buy cheap plastic toys online; they may come from countries with weaker regulations. Choose toys approved in the EU,” she advises. “Don’t put plastic cookware in the microwave. And look for personal care products with fewer additives – we should ask which chemicals are truly needed.”

More than 16 000 chemicals have been identified in plastics alone, highlighting the trade‑off between convenience and health. “We don’t want to ban all chemicals – many are genuinely useful,” she says.

“But we lack crucial information about a large number of them, even in Europe. Current tests don’t capture all health effects, so we need stronger regulation and safer material design from the start, instead of discovering problems only when it’s too late.”

Home bots step closer to reality

Picture a robot that helps an elderly person prepare meals, lifts heavy objects, or safely dismantles old gadgets. Slovenian robotics scientist Aleš Ude believes these scenarios may be closer than we think – but key challenges remain, such as equipping robots with the right levels of empathy and common sense.

General‑purpose robots that assist at home or in hospitals may be possible within a decade, says Dr Ude of the Jožef Stefan Institute in Slovenia, thanks largely to rapid advances in AI.

In the ReconCycle initiative, Ude explored how robots could take apart a wide range of electronic devices for recycling.

“Almost nobody except the rich has 24‑hour domestic help. Many people would pay considerably for such a robot,” he says. Some pilot projects already use robots to support elderly patients in hospitals.

To operate in these environments, Ude notes, robots will likely need a humanoid form: hospitals are built around human anatomy, and legs allow access to places wheeled robots cannot reach. They must also be extremely reliable, safe and robust enough to survive inevitable mishaps.

Traditional pre‑programming, which works for industrial robots, is unsuitable for the messy, unpredictable home environment. What robots lack is common sense – the ability to respond appropriately to unexpected events and avoid dangerous errors. Generative AI and neural networks, inspired by the human brain, are helping robots better navigate such uncertainty.

Ude’s team is also researching human–robot collaboration. Communication has improved dramatically with large language models, but domestic or hospital robots will need to anticipate a person’s intentions through their neural networks.

And if they are to care for sick or elderly people, a degree of empathy will be essential – something that remains an open challenge.

Robotic vacuum cleaners may be common today, but a useful domestic humanoid robot will need to handle many different tasks. What these robots will be capable of in 10 years is uncertain, Ude says. But once the technology matures, widespread adoption in homes and hospitals is likely to follow quickly.

The next pandemic: expect the unexpected

What’s next after COVID‑19? Dutch virologist Marion Koopmans argues that vigilance, data and citizen science should drive Europe’s fight against future outbreaks – and explains why pandemics are seldom predictable.

Another pandemic is inevitable, says Professor Koopmans of the Erasmus Medical Centre in Rotterdam. We don’t know when it will happen, where it will start or what form it will take – but we can still prepare.

Pandemics begin in uncertainty: in the earliest days, it is often unclear who becomes infected, how a pathogen spreads and how fast it moves. But good data, AI and contributions from ordinary citizens can help scientists and clinicians act earlier and more effectively.

When COVID‑19 hit, Koopmans was leading the Versatile Infectious Diseases Observatory (VEO), a project to design a future‑proof surveillance system for emerging diseases.

“COVID‑19 was a high‑impact pandemic, although it could have been worse. The start was messy because responding to a new disease is like building a ship while sailing it,” she says. “It takes time to get answers from studies, but rapid action is crucial. As outbreaks are accelerating globally, we need to stay alert and strengthen early warning systems.”

Recent developments show why. “We’ve just seen an mpox outbreak emerge in a forested mining region of the Democratic Republic of Congo. Outbreaks can start anywhere, and it’s unrealistic to expect clinicians to test for every possible pathogen. We need to get better at spotting anything unusual that warrants immediate investigation – especially in areas where risk is rising.”

Those risks increase where humans come into contact with animals, creating opportunities for spillover. VEO explored such scenarios by combining different types of data, for example, where migratory bird routes overlap with areas of dense poultry farming.

One major lesson from recent years, she says, is to expect the unexpected: the 2009 swine flu pandemic, for instance, emerged not in Asia, as widely assumed, but in South America.

“Our studies have highlighted several possible pathways for emergence, ranging from bird flu and West Nile virus to diseases linked to melting permafrost, and infections that could spread rapidly through major cities.”

Looking ahead, Koopmans hopes to see a global, integrated data repository drawing on scientific studies, public health surveillance and large‑scale environmental monitoring. Citizens too can play a role by reporting unusual findings, such as dead birds or new mosquito sightings.

“We’re also exploring how AI might flag potential signals from these sources, and how broad genetic detection tools could uncover new viruses in wildlife or livestock that may pose future risks.”

This article was originally published in Horizon the EU Research and Innovation Magazine.

Text: Anthony King

New research enables computer designs to incorporate the limitations of 3D printers, to better control materials’ performance in aerospace, medical, and other applications

People are increasingly turning to software to design complex material structures like airplane wings and medical implants. But as design models become more capable, our fabrication techniques haven’t kept up. Even 3D printers struggle to reliably produce the precise designs created by algorithms. The problem has led to a disconnect between the ways a material is expected to perform and how it actually works.

Now, MIT researchers have created a way for models to account for 3D printing’s limitations during the design process. In experiments, they showed their approach could be used to make materials that perform much more closely to the way they’re intended to.

“If you don’t account for these limitations, printers can either over- or under-deposit material by quite a lot, so your part becomes heavier or lighter than intended. It can also over- or underestimate the material performance significantly,” says Gilbert W. Winslow Associate Professor of Civil and Environmental Engineering Josephine Carstensen. “With our technique, you know what you’re getting in terms of performance because the numerical model and experimental results align very well.”

The approach is described in the journal Materials and Design, in an open-access paper co-authored by Carstensen and PhD student Hajin Kim-Tackowiak.

Matching theory with reality

Over the last decade, new design and fabrication technologies have transformed the way things are made, especially in industries like aerospace, automotive, and biomedical engineering, where materials must reach precise weight-to-strength ratios and other performance thresholds. In particular, 3D printing allows materials to be made with more complex internal structures.

“3D printing processes generally give us more flexibility because we don’t have to come up with forms or molds for things that would be made through more traditional means like injection molding,” Kim-Tackowiak explains.

As 3D printing has made production more precise, so have methods for designing complex material structures. One of the most advanced computational design techniques is known as topology optimization. Topology optimization has been used to generate new and often surprising material structures that can outperform conventional designs, in some cases approaching the theoretical limits of certain performance thresholds. It is currently being used to design materials with optimized stiffness and strength, maximized energy absorption, fluid permeability, and more.

But topology optimization often creates designs at extremely fine scales that 3D printers have struggled to reliably reproduce. The problem is the size of the print head that extrudes the material. If the design specifies a layer to be 0.5 millimeters thick, for instance, and the print head is only capable of extruding 1-millimeter-thick layers, the final design will be warped and imprecise.

Another problem has to do with the way 3D printers create parts, with a print head extruding a thin bead of material as it glides across the printing area, gradually building parts layer by layer. That can cause weak bonding between layers, making the part more prone to separation or failure.

The researchers sought to address the disconnect between expected and actual properties of materials that arise from those limitations.

“We thought, ‘We know these limitations in the beginning, and the field has gotten better at quantifying these limitations, so we might as well design from the get-go with that in mind,” Kim-Tackowiak says.

In previous work, Carstensen developed an algorithm that embedded information about the print nozzle size into design algorithms for beam structures. For this paper, the researchers built off that approach to incorporate the direction of the print head and the corresponding impact of weak bonding between layers. They also made it work with more complex, porous structures that can have extremely elastic properties.

The approach allows users to add variables to the design algorithms that account for the center of the bead being extruded from a print head and the exact location of the weaker bonding region between layers. The approach also automatically dictates the path the print head should take during production.

The researchers used their technique to create a series of repeating 2D designs with various sizes of hollow pores, or densities. They compared those creations to materials made using traditional topology optimization designs of the same densities.

In tests, the traditionally designed materials deviated from their intended mechanical performance more than materials designed using the researchers’ new technique at material densities under 70 percent. The researchers also found that conventional designs consistently over-deposited material during fabrication. Overall, the researchers’ approach led to parts with more reliable performance at most densities.

“One of the challenges of topology optimization has been that you need a lot of expertise to get good results, so that once you take the designs off the computer, the materials behave the way you thought they would,” Carstensen says. “We’re trying to make it easy to get these high-fidelity products.”

Scaling a new design approach

The researchers believe this is the first time a design technique has accounted for both the print head size and weak bonding between layers.

“When you design something, you should use as much context as possible,” Kim-Tackowiak says. “It was rewarding to see that putting more context into the design process makes your final materials more accurate. It means there are fewer surprises. Especially when we’re putting so much more computational resources into these designs, it’s nice to see we can correlate what comes out of the computer with what comes out of the production process.”

In future work, the researchers hope to improve their method for higher material densities and for different kinds of materials like cement and ceramics. Still, they said their approach offered an improvement over existing techniques, which often require experienced 3D printing specialists to help account for the limitations of the machines and materials.

“It was cool to see that just by putting in the size of your deposition and the bonding property values, you get designs that would have required the consultation of somebody who’s worked in the space for years,” Kim-Tackowiak says.

The researchers say the work paves the way to design with more materials.

“We’d like to see this enable the use of materials that people have disregarded because printing with them has led to issues,” Kim-Tackowiak says. “Now we can leverage those properties or work with those quirks as opposed to just not using all the material options we have at our disposal.”

Humans can track a handful of objects visually, but their imaginations can only handle one

Human beings can juggle up to 10 balls at once. But how many can they move through the air with their imaginations?

The answer, published last month in Nature Communications, astonished even the researchers pursuing the question. The cognitive psychologists found people could easily imagine the trajectory of a single ball after it disappeared. But the imagination couldn’t simultaneously keep tabs on two moving balls that fell from view.

“We set out to test the capacity limits of the imagination, and we found that it was one,” said co-author Tomer D. Ullman, associate professor in the Department of Psychology. “I found this surprising, so I can understand if others do, too.”

Ullman, who heads Harvard’s Computation, Cognition, and Development lab, has a long-time interest in what is known as intuitive physics. Think of the brain conjuring a ball as it rolls downhill, or sounding the alarm over two objects on a sure-fire collision course.

“How do we interact with the physical world around us?” wondered Ullman, who is also affiliated with the Kempner Institute for the Study of Natural and Artificial Intelligence. “I subscribe to the theory that the brain may be running mental simulations, kind of like a video game.”

These couldn’t be perfect simulations of physical environments, right down to the level of atoms and molecules. So Ullman’s lab has worked to understand what kinds of hacks and workarounds make mental simulations possible.

“The human imagination is just really cool, and we find a lot of people are quite interested in how it works,” he offered.

A sizable body of research has explored the capacity limits of human perception, or how many objects the brain can track in a visual scene. “Maybe you’re a parent watching multiple kids, or maybe you’re a lifeguard on duty,” Ullman said. “Obviously you can’t keep track of everything.”

Neuroscientists, psychologists, and computational modelers have found visual tracking is limited to just a handful of moving objects. But few have explored the imagination’s capacity limits.

In the new study, online participants were shown an animation of a bouncing ball, as if on a racquetball court, before it vanished. Others saw two balls ricocheting at completely different cadences before both disappeared. Designing the experiments with Ullman was lead author Halely Balaban, an assistant professor of cognitive psychology at the Open University of Israel.

Also devised were two computational models to explain how the imagination might follow these invisible balls to their moment of impact. The first model posited that multiple objects would be moved in parallel, while the second envisioned independently moving each ball in more of a serial fashion.

Ullman and Balaban found their online recruits were pretty good at predicting when a single invisible ball would have hit the ground. But people fumbled at tracking two.

“It was harder than any of us expected,” said Ullman, noting how reliably the exercise produced laughs.

Based on past findings, the co-authors originally thought the imagination could probably track about three or four objects. There were also intuitive reasons to think the mind’s eye could move multiple objects in parallel.

“If I close my eyes right now, I can see a tower of blocks falling down,” Ullman noted. “It doesn’t feel limited. People feel like they should be able to move more than one.”

In fact, a follow-up experiment found people were slightly better at tracking two balls that moved in tandem before disappearing. But performances still paled next to a yet another follow-up, in which study participants tracked two balls that remained visible until impact.

When it comes to tracking objects that have disappeared, the researchers found, the human imagination relies largely on a serial model, moving each piece one after the other.

A separate follow-up tested whether people might be conserving mental energy by employing a serial model. After all, running a simulation via the parallel model would require more effort. Imagine a computer running multiple simulations at once.

“We offered participants a bunch of money if they could get this right,” Ullman explained. “But that didn’t seem to matter.”

For Ullman, the findings open an exciting frontier. “There has been decades and decades of work on how the mind uses clever tricks to keep track of what’s in front of you,” he said. “But there’s been so little on the tricks and limitations of the mind’s eye. I could imagine a lot more work to do here.”

Text: Christy DeSmith (Harvard Staff Writer)

Image: Tomer D. Ullman.Niles Singer/Harvard Staff Photographer)