Last fall I had the chance to do an internship in the Alpine Remote Sensing group at SLF Davos, the snow and avalanche research institute. I had just completed two years of the Computer Science Bachelor and wanted to try something new. Throughout my internship, I worked on automatically detecting and classifying avalanches in RGB webcam images. This work aims at improving avalanche monitoring by leveraging computer vision techniques, making it easier to identify hazardous conditions in real time. (RGB cameras are designed specifically to capture visible light. They create images that replicate human vision, capturing light in red, green and blue wavelengths (RGB) for accurate color representation.)

Pia Ruttner-Jansen, who supervised me during my project at SLF, works on capturing fine-grained snow-depth distributions using automated measurement stations. This data is a valuable input for assessing avalanche risk. With higher precision, preventative measures such as road closures can be shorter, more targeted, and ultimately improve safety while reducing downtime. Each station is installed high in the mountains, directly facing a slope and equipped with LiDAR sensors. Although this type of sensor produces noisier data than commercial laser sensors, it is far more affordable, making it possible to deploy multiple stations at strategic locations.

Apart from a LiDAR sensor each measurement station also has an RGB camera that is set up to take a photo every hour. While not directly used to compute snow-depth distributions these images provide an additional, underutilised stream of data. The goal of my project was to use this data to automatically detect new avalanches and reduce the time spent manually clicking through images. In addition to learning about different types of avalanches and labeling images my work mainly involved experimenting with different computer vision approaches. Two major challenges were using a noisy dataset with only a small amount of unique avalanche events and choosing a suitable task that is useful for solving the problem while allowing for the use of already labeled data.

SLF has a truly unique working culture: when it first started snowing, the head of the institute sent out an email to celebrate this with free chocolate for everyone. Many PhD students have “Jochdienst” instead of teaching duty. In a weekly rotation, they take snow measurements up at Weissfluhjoch at 7:30 a.m. whatever the weather. Fascinatingly, it is the only place at this height on earth where there have been continuous daily measurements of fresh snow and snow depth for more than 80 years!

I found it very refreshing to experience a new environment. At ETH, I would usually mostly interact with other Computer Science students. Instead, the people I met at SLF had a background in Geomatics, Geology, Electrical or Mechanical Engineering, and worked on various problems such as mapping avalanches, studying avalanche dynamics or detecting cracks in borehole images. Everyone was super nice and helpful. The amazing cafeteria constituted a great way to chat with others and find out what they are working on.

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.

Experience the Swiss mountain air, discover Zurich’s Old Town, and work on cutting-edge research projects. The ETH Student Summer Research Fellowship Programme (ETH SSRF), run by the Department of Computer Science, offers this opportunity to around 20 students from all over the world each year. Over a period of two months, programme participants gain initial experience within a research group and enjoy an exciting range of additional activities.  From mountain hikes and trips to the chocolate museum to visits to the Swiss National Supercomputing Centre (CSCS) in Lugano, the experiences on offer from the beginning of July to the end of August are diverse.

However, the programme has a strong research focus. Students can apply for one of nine specialisms. The options cover the entire spectrum of computer science, from machine learning and programming languages to robotics and bioinformatics.

Ambition as proof

One of this year’s ETH SSRF 2025 participants is Clijo Jose. Hailing from Delhi in India, he is currently pursuing a Master’s degree in Applied Mathematics and Statistics at the Institut Polytechnique de Paris. Initially, he was unsure about applying for the SSRF programme. “I had only dealt with computer science to a limited extent during my studies,” he explains. However, during his first year of the master’s programme, he encountered machine learning and quickly developed a passion for it.

“You don’t have to know everything about your research idea already in advance. The programme offers enough time to formulate and develop the idea afterwards.”

Clijo Jose, SSRF student from India

During his time in Zurich, Clijo was a member of the Natural Language Processing and Educational Technologies Lab research group led by Professor Mrinmaya Sachan. There, he compared the natural and formal language reasoning of LLMs. More specifically, he compared the mathematical reasoning abilities of LLMs like ChatGPT in “Lean” and in English.

Lean is an open-source proof assistant and programming language that assists computer scientists and mathematicians in creating and verifying proofs interactively. “It makes it possible to formally verify a mathematical proof step by step,” Clijo explains. However, “Lean” does not understand natural languages such as English, but only a precise, structured input in the Lean language that combines logic, mathematics and programming constructs.

On the other hand, LLMs such as ChatGPT typically attempt to solve mathematical problems in natural language, but can also be used to synthesise proofs in “Lean” just like code generation. Clijo is working on a dataset and evaluation rubric to compare the performance of LLMs in English and in Lean.

According to Clijo, you should have a rough idea of your research project before the SSRF programme begins. “That doesn’t mean you have to know everything about it already. The programme offers enough time to formulate and develop the idea afterwards,” he explains. “It’s important to show ambition at times like these.”

Clijo particularly enjoyed hiking in Switzerland. “The country has so many wonderful places to visit. At the weekends, when I had time, I wanted to visit as many of them as possible with my friends and marvel at the beautiful lakes and landscapes,” he says.

Bursting the bubble

Matthijs Jansen Op de Haar has had big ambitions for a long time. He once wanted to become a doctor and was passionate about biology. “One day, I tried my hand at programming, which sparked a deep and profound interest in computer science,” he says. Originally from the Netherlands, Matthijs is currently studying for two separate bachelor’s degrees at the University of Twente: technical computer science and business information technology. “During my studies, I realised how diverse computer science is. It can be used across disciplines, whether in finance, biology or history,” he explains.

“Applying for the SSRF programme was my way of bursting my own bubble. It allowed me to broaden my horizons, meet new people and tread closer to my limits.”

Matthijs Jansen Op de Haar, SSRF student from the Netherlands

He works in Professor Mennatallah El-Assady’s Interaction, Visualisation and Intelligence Augmentation research group. There, he conducts research on so-called “mixed-initiative visual analytics systems”. In this context, “mixed initiative” means that a human and a software agent (e.g. a large language model) can both take initiative in carrying out a shared task or objective. And “visual analytics” refers to the domain which studies the visual knowledge representation of data.

Matthijs and his supervisor are investigating the domains in which researchers have already implemented these systems. “We are trying to determine the areas in which such systems – in the future – could prove particularly promising,” he explains.

As soon as he arrived in Zurich, a day before starting research, Matthijs spent an entire day exploring the city with his new friends from the SSRF programme. “We got an overview of the city and visited the Kunsthaus and the Landesmuseum, among other places, and tried lots of different foods,” says Matthijs.

He particularly appreciated the opportunity to exchange ideas with motivated and talented people. “It’s generally easy to get caught up in a bubble and be complacent with your current way of life, the people around you and your own world view. Applying for the SSRF programme was my way of bursting that bubble, allowing me to broaden my horizons, meet new people and tread closer to my limits,” he says.

An efficient algorithm for fighting tumours

Urša Zevnik from Slovenia greatly enjoyed the social aspect of the SSRF programme, too. “I had many great encounters and spontaneous experiences,” she says. For example, she was out and about in Zurich with others, intending only to buy groceries, but the day ended with them spending over two hours marvelling at the fireworks for Swiss National Day.

Urša also greatly appreciated the other SSRF students. “We did all these great things together: we tried fondue, visited different Swiss cities and participated in a swim race in the Limmat River in Zurich. Thanks to this whole experience, I made friends for life,” she says.

“The SRRF programme gave me the opportunity to challenge myself in computational problem-solving, which felt almost like playing a strategy game.”

Urša Zevnik, SSRF student from Slovenia

Urša completed her Bachelor’s degree in Biochemistry. “After that, I wanted to broaden my horizons,” she says. During her gap year, she gained research experience in the pharmaceutical industry. She then began a master’s degree in Stockholm combining biomedicine and bioinformatics. “I think it will become increasingly important in the future to apply computer approaches to other sciences,” she says.

In the Biomedical Informatics Group, led by Professor Gunnar Rätsch, Urša is working on a tool that identifies new targets, known as “epitopes”, for cancer immunotherapy. Epitopes are short protein fragments presented on cell surfaces and recognised by the immune system. The algorithm that Urša is helping to develop is designed to identify epitopes that are specific to cancer cells and can therefore be used to activate the immune system to attack only the tumour. “The danger lies in the fact that if a chosen epitope is also present in healthy cells, the therapy could damage healthy organs,” Urša explains.

Urša’s lack of formal computer science training is not an obstacle for her. “The SRRF programme gave me the opportunity to challenge myself in computational problem-solving, which felt almost like playing a strategy game,” Urša says. “It was engaging and fun, and it solidified my decision to pursue a PhD that applies computational approaches to medical research,” she says.

A single letter becomes a hard problem

Violeta Kastreva also gained valuable experience during her time in Zurich. “I learned more in those two months than I usually do in a whole year,” she says.

Originally from Bulgaria, she is currently studying for a bachelor’s degree at Sofia University St. Kliment Ohridski. As a child, she wanted to become an artist. However, she developed a keen interest in mathematics at an early age, and by the time she was in ninth grade, she had become passionate about computer science, especially theoretical computer science. “I like this kind of theoretical research. It’s not about celebrating small successes day after day, but rather a lengthy process that one day leads to a brilliant idea,” says Violeta.

“The programme feels very challenging, especially at the beginning. You might even think that you’re not good enough for the programme. But if you’ve been selected, you’ve earned your place.”

Violeta Kastreva, SSRF student from Bulgaria

Together with her team in the Algorithms and Didactics Group, led by Professor Dennis Komm, Violeta worked on a project to prove that unary tokenisation is an NP-hard problem.

To oversimplify, NP-hard means it belongs to a class of computational problems for which no efficient solution algorithm has been found. Tokenisation, on the other hand, involves breaking down a sequence of symbols into units called tokens. For instance, if the letters “ABC” frequently appear together, they can be combined into a single token. In unary tokenisation, the “text” uses only one character (for example, “AAAA”). Even in this simple setting, choosing the best split into tokens is NP-hard. She succeeded in establishing this result during her stay in Zurich.

Afterwards, Violeta and her team continued to explore related problems, extending the scope of the work. This contribution is relevant not only to theoretical computer science but also to machine learning, where understanding computational limits helps guide the design of practical algorithms.

Violeta advises future participants in the SSRF programme not to put too much pressure on themselves with regard to their research. “Everything feels very challenging, especially at the beginning. You might even think that you’re not good enough for the programme. But if you’ve been selected, you’ve earned your place,” she says.

During her time at ETH Zurich, she learned how challenging the work of a researcher can be. “It’s not like homework, where you know a solution exists,” she says. “Instead, you often work on things that you can’t be sure actually exist.” However, according to her, you can always bear in mind that your research may contribute to the further development of computer science. Even if you don’t have any groundbreaking results, that’s not a problem. “The lessons you’ve learned along the way are already very valuable,” she says.

This article written by Olivier Nüesch was originally published by Department of Computer Science at ETH Zurich.

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!

In 2015, we, Anna Flurina Kälin and Julia Schicker, met on our first day of our Bachelor’s programme in Computer Science. Within the first few minutes of our conversation, we realised that we did not only share interests in the technical field, but that both of us had previously completed degrees at art schools. It only took a few months for our idea to form: A series of artists talks at ETH, inviting creatives using any part of computer science as their medium and have them present their work in front of a mixed audience of students and faculty from ETH as well as art enthusiasts from Zurich and beyond. Seeing that many students in our course only ever saw more traditional art forms, we wanted to bring artists closer who investigate questions that are intertwined with ETH’s research.

When we finally took the courage to send an email to the Department of Computer Science, we received a reply connecting us with Bob Sumner from the Game Technology Center. Together with Fábio Porfirio, Bob has helped us organise the events and the Department has generously supported us since then.

In October 2018, the first Art & Computer Science Talk took place. Mario Klingemann, an acclaimed artist exploring the creative potential of machine learning for many years, gave the first talk in front of an audience of roughly one hundred listeners, confirming that there was a vast interest for this.

Since then, we’ve held talks once every semester (except for the break during the pandemic), inviting artists exploring surveillance, blockchain applications, interactivity and many more topics. Since 2023 and thanks to the efforts of Adrian Notz, curator of AI + Art at the ETH AI Center, we also visit labs with the artists to bring them in touch with researchers.

On May 15th, our next talk is going to take place. This time, we’ve invited Lundahl & Seitl, a Stockholm-based artist duo. They specialise in creating shared experiences that allow one to perceive the world differently by targeting our senses in unusual ways utilising immersive spaces. They use virtual reality not only as a tool to simply create non-existing spaces, but to challenge notions of the real and negotiate the relationships to our surroundings.

The work River Biographies, 2022 – ongoing, for example, creates group choreographies by pairing individuals where one guides the other. With this, they emphasise communication and trust, reexamining our connection to our surroundings. The group embodies natural elements, forming a river together and exploring how rocks and waters interact.

Lundahl & Seitl’s work has been exhibited widely, such as Centre Pompidou Metz, Gropius Bau and Staatstheater Kassel, and they were visiting artists at the ACT Programme at MIT in 2022. We are very much looking forward to welcoming Lundahl & Seitl to ETH as well as you to their artist talk!

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