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EPFL neuroscientist maps brain lipids and wins 2026 Eppendorf–Science Prize
Gioele La Manno reveals 539 lipid-defined brain regions and uncouples lipid organization from gene expression
Celebrating Women in Science Day 2026 at CERN
Every year, on 11 February, CERN celebrates the International Day of Women and Girls in Science. In an effort to amplify diverse voices in STEM, six women scientists from CERN open up about their careers and the lessons they hope to pass on to the next generation. Francesca is an Italian biomedical engineer pursuing her PhD at CERN, working in the robotics team of the Beams Department. She’s currently working on the MARCHESE project, in affiliation with Campus Bio-Medico University of Rome, focusing on contactless monitoring of physiological parameters. Despite choosing a scientific career and often volunteering in scientific outreach initiatives, Francesca continues to nurture her passion for the humanities and arts. “I firmly believe that collaboration and inclusion are the right paths to improve the world together. I’m driven by a strong curiosity about everything around me.” Berare is a Turkish experimental physicist who worked with the ATLAS experiment during her MSc and PhD on searches for physics beyond the Standard Model. During her PhD, she became a Support Scientist for the Beamline for Schools (BL4S) competition, which marked the beginning of her journey combining research with science communication. She now works for both BL4S and ELISA, the mini proton accelerator at CERN’s Science Gateway. She fondly remembers the moment when the BL4S 2022 winners arrived at the experimental zone: “I felt incredibly lucky to share my passion with young, curious and really passionate people. We spent two weeks of beam time together watching their ideas turn into reality. All the intricate details of running an experiment, we accomplished them as a team.” Mia is a Canadian research fellow in the Accelerator Systems Department, working on beam developments for fundamental physics with radioactive molecules. Mia pursued a bachelor’s degree in mechanical engineering before moving to Vancouver for a master’s degree in engineering physics. Her interest in radioactive ion beam production brought her to CERN, where she studied actinide beams for three years as a PhD student in the Marie Curie European Training network “LISA” (Laser Ionisation and Spectroscopy of Actinides) at ISOLDE – a facility at CERN where scientists study the properties of atomic nuclei, with further applications in fundamental studies, astrophysics, and material and life sciences. Laura is a German mechanical engineer at CERN working on research and development to produce cavities for future accelerators. She studied mechanical and aerospace engineering at the Technical University of Munich. She joined CERN four years ago as a technical student for her master’s thesis and continued as a fellow working on collimators. Laura says that “as a child, I took part in girls’ day initiatives, which sparked my fascination for astrophysics and engineering. Later, it felt natural for me to give something back by organising workshops for young students to make STEM seem less intimidating and more accessible.” She particularly enjoys being close to the CERN Main Workshop and understanding how design ideas become real components. Paraskevi is a Greek physicist working at CERN’s HiRadMat (High-Radiation to Materials) facility, designed to deliver high-intensity beams to an irradiation area where material samples, as well as accelerator component assemblies, can be tested. She studied physics at NKUA, specialising in nuclear and particle physics, and is now pursuing her PhD at HiRadMat in collaboration with the University of Liverpool, studying novel materials for high-intensity beam diagnostics. “Continuous learning is one of my biggest motivators, and I am looking forward to exploring the limits of our knowledge on beam-material interactions,” says Paraskevi. Silvia is a Spanish physicist and deputy project manager for the Muon Drift Tube of the CMS detector, a general-purpose detector at the Large Hadron Collider (LHC). She started her career at CERN more than 20 years ago as a technical student, working on prototypes of the first dipoles for the LHC. “My first motivation came in high school, from my teacher,” says Silvia about what made her pursue a career in STEM. “My favourite part about my job is learning. You can learn something new every day.” In parallel to this initiative, like every year since 2017, from 2 to 6 February 66 women scientists and engineers visited schools in Geneva, Switzerland, and in the Haute-Savoie and Ain regions of France to meet with students. Organised in collaboration with CERN, the University of Geneva, EPFL, LAPP and LAPTh, this event allowed more than 4200 students between 6 and 17 years old to meet a woman scientist, discuss their profession and career path, ask questions and participate in classroom activities.
Is AI more a partner, competitor, or crutch for biomedical scientists?
Biologist Paul Knoepfler asks: Could overdependence on AI make us less capable as researchers?
How should European scientists respond to the US AI kill switch?
Earlier in June, Europe’s worst fears of technological dependence were confirmed when the White House ordered the AI firm Anthropic to stop non-US citizens using its leading models. This kill-switch moment crystalised anxieties in Brussels that the continent, lagging the US on the creation of leading-edge AI models, is now a technological of vassal of Washington. The shock has specific implications for European scientists and universities. Some governments are now worried that with AI being integrated into labs, Donald Trump now has his finger hovering over an off switch for European science. “Having restrictions on this tool, it can have an impact on our own research,” said France’s research minister, Philippe Baptiste, at the VivaTech conference in Paris on June 18.Baptiste, himself a former academic, said that colleagues in mathematics had recently warned him they were now cut off from leading-edge US models, risking their work. “This model is not available anymore now because of the US restriction, and for the first time this had an impact on mathematics,” a field where large language models (LLMs) are making a significant contribution, said Baptiste. So, should European scientists avoid relying on US models in the future? Or would this be a self-harming overreaction? And how is AI being used in science, anyway? What happened?On June 12, it emerged that Washington, citing cybersecurity concerns, had ordered Anthropic to stop non-US citizens using two models: Claude Mythos 5, and Claude Fable 5, a version of Mythos but with guardrails to prevent abuse. Anthropic said Washington’s concerns were overblown. But forced to follow the order, yet with no way to check who was using the models, the company suddenly shut down access for everyone, US citizens included. There’s still no agreement on how to restore broad access. But last week, Bloomberg reported that select US companies, and the US government, still had access to Mythos despite the ban, but the EU’s cybersecurity agency Enisa had been shut out. Class division of researchersIn the longer term, access outside the US should be restored, said Marcel Salathé, co-director of the AI centre at the Federal Polytechnic School of Lausanne (EPFL), because a permanent restriction would be financially ruinous to US AI firms. “Even Trump will eventually budge to economic pressure,” he said.However, US AI firms might end up operating rather like banks, doing due diligence on users of their leading-edge AI models, he went on.European scientists are unlikely to fall foul of such checks, he said. But having a Chinese postgraduate in their lab, or doing a type of research the US disapproved of, could potentially result in access being denied. “It will create a sort of class division of researchers,” he said. More than frontier modelsHowever, successfully deploying AI in science is about far more than just having access to leading LLMs such as Mythos.Instead, many science-specific tools, most famously Alphafold, which predicts protein folding structures, require “far less” computing power to train than frontier models, well within the capabilities of European supercomputers, according to a recent analysis by the Arq Foundation, a Brussel-based AI think tank. Instead, tools such as Alphafold are possible because certain fields, like protein folding, weather prediction or astronomy, have large, clean, standardised datasets that can be used to train predictive models. Rather than compute, “data is the central constraint for AI for science,” the report says. For example, a less powerful physics model trained on CERN’s data “will be more valuable than the best model without certain data,” said Bengüsu Özcan, author of the report. European advantage?Europe could therefore steal a march by using its scientific data and infrastructure to create models US digital giants cannot. “They might actually match or go even beyond what these big [US] companies are doing,” said Özcan.Indeed, some European start-ups hope to create automated testing labs to create the kind of standardised data that could speed up the discovery of new materials. Materials scientists say there’s currently too little data to train models. AI scientistsHowever, even if the bottleneck for AI in science is often data, rather than compute, being at the mercy of the US for LLM access could still become a problem for European scientists. A new class of “AI scientists” based on leading LLMs is also emerging, Özcan’s report says.Unlike domain-specific tools like Alphafold, these try to automate day-to-day scientific work, by providing an overarching tool that coordinates specific models to synthesise existing literature, generate hypotheses or run analysis of data. The problem for Europe, however, is that these tools are sometimes based on US leading models. Google Deepmind’s AI Co-Scientist, for example, is built on Google’s Gemini LLM. Systematic failureThese scientific automation tools are still in their early stages, and still exhibit “systematic failures on scientific tasks,” Özcan’s report says. One of the problems is that the messy, day-to-day process of scientific discovery is largely not documented, meaning LLMs don’t have a good source of data to learn how scientists actually work. Meanwhile, some tasks, such as literature synthesis, don’t typically require access to leading edge models.However, the performance of AI scientific assistants could improve as companies such as Google pour resources into developing them. Rely on US“Unfortunately, for scientific assistants, there is not much we can do right now except rely on US frontier AI,” said Antoine Bosselut, a natural language processing expert based at EPFL. Chinese-built open models, which can run on local computers, lessening the risk of a shutdown, are touted as one alternative to US dependence. There’s also Mistral, once seen as Europe’s main AI hope, but the Paris-based company has seen its recent models fall behind in performance. But the Chinese alternatives are generally not as capable as US frontier AI models, said Bosselut, unless paired with fiddly, custom tools that allows them to act as independent agents. “Most scientists do not have the time,” he said. Don’t jump ship yetAlthough the kill switch moment is seen a warning shot for European scientists, it doesn’t mean labs should abandon US AI tools immediately. “I do not think the right response is simply to abandon US-based models and systems under the banner of sovereignty or independence,” said Mattias Björnmalm, secretary general of the Cesaer university association. “That would be an overreaction.”Related articlesEuropean sovereignty in AI requires ‘ugly trade-offs,’ say experts Robotic lab start-up makes case for €500M EU-funded materials testing facilityAI in science: is it useful? Instead, European researchers need to ask themselves whether they’re becoming dependent on technology that could be turned off in Washington, said Özcan. “They might not see this today, but they should really start asking this question,” she said. European researchers need a plan B, said Salathé. “I would recommend to every researcher, very proactively explore alternatives.”Own toolsIn addition, Björnmalm and others want European research institutions to develop their own scientific AI tools.Instead of signing AI deals with US digital giants, European universities should be using this money to create their own models, which may end up being superior for research, said Sabine Leonelli, an expert on the use of AI in science at the Technical University of Munich. “Universities in Europe seem to be happy to sign commercial contracts with US companies on the assumption [their AI tools] are better,” she said. But US frontier LLMs are typically opaque, and have been developed without independent scientific input, she said. “This is not good enough for science.”Last year, Swiss researchers released Apertus, an LLM designed to be completely transparent, from its development methods to its training data. The idea is that this makes scientific tools built on top of the model far more reproducible than with closed, commercial alternatives.“Europe needs more of this: serious capacity-building that gives researchers, universities and companies credible options,” said Björnmalm.
EPFL will celebrate its Healthy Campus certification during its upcoming "Health Days"
EPFL (Ecole Polytechnique Fédérale de Lausanne) has received the FISU’s Healthy Campus label with the highest certification level—platinum. It earned a score of 99 out of 100 in recognition of its comprehensive commitment to health. EPFL has officially—and with honours—joined the ranks of universities that make the health of their community a daily priority. To […]