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IBM Research Europe

Zurich, switzerland.

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Senior Research Scientist

Distributed Computer Systems

Ref. 2023_026

IBM Research Europe – Zurich is one of IBM’s 12 global research labs. Our mission comprises cutting-edge research for tomorrow’s information technology and inventing what’s next in computing. Our activities include applied research in the areas of Hybrid Cloud, AI, Security, and Quantum computing as well as fundamental science research. We cultivate close relationships with academic and industrial partners and are part of the local and global scientific community to help drive Europe’s innovation agenda. We are seeking an outstanding and passionate Senior Research Scientist to join our team. You will play an essential role in developing bleeding-edge cloud-first storage solutions and supporting our research teams in a highly collaborative and international research environment. If you're excited about driving groundbreaking systems work, solving complex problems, and supporting other team members, we want to hear from you.

Your responsibilities will include:

• Being the technical lead of a team building and enhancing IBM products and technologies.
• Mentoring both researchers and software developers, offering guidance and experienced thinking.
• Collaborating with external industry and academic partners and applying for external funding.

The minimum requirements for this role are as follows:

• Ph.D. in computer science coupled with a strong publication record in a relevant systems area.
• At least 5 years of professional experience after graduation.
• Experience leading either research or development teams.

To excel in this role, the following qualifications are desired :

• Hands-on experience with distributed storage systems (e.g., Ceph, Spectrum Scale).
• Deep understanding of the modern IO stack (e.g., io_uring/SPDK, CXL, ZNS/FDB storage extensions).
• Knowledge of modern non-volatile media technologies and the internals of NVMe SSDs.
• Experience with cloud virtualization technologies such as the Kubernetes or OpenShift platforms.
• Strong teamwork skills and a desire to contribute and help proactively.

IBM is committed to diversity at the workplace. With us you will find an open, multicultural environment. Excellent flexible working arrangements enable all genders to strike the desired balance between their professional development and their personal lives.

How to apply

If you are excited to be a part of IBM Research's mission to shape the future of technology, please submit your CV and motivation reasons.

For technical questions, please contact Dr. Radu Stoica, Manager Infrastructure Software at [email protected] .

• United States
• Science of Quantum & Information Technology
• Security Research
• Hybrid Cloud Research
• Accelerated Discovery & AI
• Binnig & Rohrer Nanotechnology Center
• Careers @ the Zurich Lab
• Think Lab Zurich
• EU projects
• Great Minds internships
• Research Frontiers Institute
• Background & historical highlights
• News archive

IBM Research Europe

Zurich, switzerland.

Connect with us:    

IBM Instagram IBM Research Facebook IBM Research Twitter IBM Research YouTube IBM Research Blog IBM Research Blog

Pre-doctoral position

AI Automation

Ref. 2024_005

Project Description

Robots such as SPOT and Unitree A1 have reached outstanding performance in mobility. However, utilization of such devices is still limited in practical visual inspection applications, due to the lack of standardization and automation in processing of images and videos captured with such devices. The Doctorate Candidate will work towards automating the visual inspection pipeline for these robots to be fully operational. At IBM Research the Doctorate Candidate will develop AI algorithms to automate the creation of new domain-specific models using the Robots, as well as automate the domain adaptation process, utilizing Large Vision Models (LVMs). Among other activities, the Doctorate Candidate will study how to automate the selection of relevant frames from long sequence of videos, use self-supervised pipelines to build reusable models with nonannotated data, or interactive visual-language prompting techniques to fine-tune the models. At ETH the Doctorate Candidate will port the AI models on the robot and experiment with multi-modal data collection modalities (e.g., infrared, thermal hyperspectral images). In addition, the student will spend a few months at Politecnico di Milano to experiment on real applications supporting data acquisition for the development of the models as well as the validation of the results. We envision the student will spend 2/3 of the time at IBM Research, 1/3 at ETH Zurich and a few months at Politecnico di Milano.

Why do your PhD at IBM Research

A unique aspect of our projects is the opportunity to work on client data that are not available to the public and represent a big challenge even for the most successful state-of-the-art methods. As part of our team, you will collaborate with experienced Research Scientists and AI Software Engineers that will lead and help you to successfully complete the challenges of the proposed task. You will also have access to HPC and Cloud infrastructure equipped with recent variants of GPUs and many other resources and tools to perform the work. The technology created in our team is powering IBM mainstream products, such as Maximo Visual Inspection and soon WatsonX . We have successfully inspected the 3rd longest suspension bridge in the world - the Storebaelt . By automating and improving inspection of civil infrastructure, we make such infrastructures safer and at the same time we extend their lifetime expectation, thus reducing CO2 emissions caused by the use of concrete for major repairs, decommission of old structures, and construction of new ones.

Candidate qualification requirements

Minimum qualifications (Mandatory)

• Outstanding university track record, with background in Computing, Machine Learning, Mathematics, Statistics, or equivalent fields;
• 3+ years of proved programming experience in C/C++ and/or Python;
• Proficient in UNIX/Linux;
• Ability to speak and write in English fluently;
• Team player, self-motivated with a passion for technology and innovation

Preferred qualifications

• Practical experience with Machine Learning and/or Deep Learning frameworks, such as PyTorch;
• Experience with one or more of the following:REST APIs, machine learning, deep learning, algorithms and data structures, test automation, distributed computing, CI/CD
• Independent worker with the ability to effectively operate with flexibility in a fast-paced, constantly evolving team environment

Nice to have

• Contribution to open source projects;
• Proved record of participation in Kaggle competitions (or similar);
• Publications in top AI conferences (NeurIPS, AAAI, etc.);
• Experience with public Cloud environments.

Additional Eligibility Criteria

• At the date of the recruitment they must not possess a doctoral degree
• They must comply with the mobility rule that is: they must not have resided or carried out their main activity (work, studies, etc.) in the country of the recruiting beneficiary (Switzerland) for more than 12 months in the 36 months immediately before their recruitment date.
• They must comply with the profile described for the position
• The admission requirements at ETH Zurich are a Master’s degree from a recognized university in Engineering or in fields closely related to the project's subject matter and excellent academic performance

IBM is committed to diversity at the workplace. With us you will find an open, multicultural environment. Excellent flexible working arrangements enable all genders to strike the desired balance between their professional development and their personal lives.

How to apply

If you are interested in this exciting position, please apply to DC6 (Robots, such as Boston Dynamics SPOT) through the following centralized application portal:

The interview process will include technical discussions and a coding interview. If you need any information about the position please contact: Dr. Cristiano Malossi, [email protected] Dr. Florian Scheidegger, [email protected] Dr. Michele Magno, [email protected]

References [1] M. Dueñas-Dı́ez and J. Pérez-Mercader, “How chemistry computes: language recognition by non-biochemical chemical automata. From finite automata to turing machines,” IScience, vol. 19, pp. 514–526, 2019. [2] Woźniak, Stanisław, et al. "Deep learning incorporating biologically inspired neural dynamics and in-memory computing." Nat. Mach. Intell., vol. 2, June 2020, pp. 325-36, doi:10.1038/s42256-020-0187-0. [3] Maass, Wolfgang. "Liquid state machines: motivation, theory, and applications." Computability in context: computation and logic in the real world (2011): 275-296.

• Abbas Rahimi

Research Interests:

My research interests lie in codesigning algorithms and emerging hardware systems with an emphasis on improving energy efficiency and robustness. These include brain-inspired computing, neuro-symbolic AI, distributed embedded intelligent systems, and in general approximation opportunities in computation, communication, sensing, and storage.

Short Biography:

I received the B.S. degree in computer engineering from the University of Tehran in 2010, and the M.S. and Ph.D. degrees in computer science and engineering from the University of California San Diego in 2015, followed by postdoctoral researches at the University of California Berkeley, and at the ETH Zürich. In 2020, I have joined the IBM Research-Zürich laboratory as a Research Staff Member.

I received the 2015 Outstanding Dissertation Award in the area of 'New Directions in Embedded System Design and Embedded Software'' from the European Design and Automation Association, and the ETH Zürich Postdoctoral Fellowship in 2017. I was a co-recipient of the Best Paper Nominations at DAC (2013) and DATE (2019), and the Best Paper Awards at BICT (2017), BioCAS (2018), and IBM's Pat Goldberg Memorial (2020).

Selected Publications:

N. Menet, M. Hersche, K. Karunaratne, L. Benini, A. Sebastian, A. Rahimi, ' MIMONets: multiple-input-multiple-output neural networks exploiting computation in superposition ' Conference on Neural Information Processing Systems (NeurIPS) , 2023.

M. Hersche, M. Zeqiri, L. Benini, A. Sebastian, A. Rahimi, ' A neuro-vector-symbolic architecture for solving Raven’s progressive matrices ', Nature Machine Intelligence , 2023.

J. Langenegger, G. Karunaratne, M. Hersche, L. Benini, A. Sebastian, A. Rahimi, ' In-memory factorization of holographic perceptual representations ', Nature Nanotechnology , 2023.

M. Hersche, G. Karunaratne, G. Cherubini, L. Benini, A. Sebastian, A. Rahimi, ' Constrained few-shot class-incremental learning ',   Conference on Computer Vision and Pattern Recognition (CVPR) , 2022.

G. Karunaratne, M. Schmuck, M. Le Gallo, G. Cherubini, L. Benini, A. Sebastian, A. Rahimi, ' Robust high-dimensional memory-augmented neural networks ', Nature Communications , 2021. (Featured in the 50 best articles in the Applied Physics and Mathematics)

A. Moin, A. Zhou, A. Rahimi, et al., ' A wearable biosensing system with in-sensor adaptive machine learning for hand gesture recognition ', Nature Electronics , 2021.

G. Karunaratne, M. Le Gallo, G. Cherubini, L. Benini, A. Rahimi, A. Sebastian, ' In-memory hyperdimensional computing ', Nature Electronics , 2020. (Appeared on the cover June issue 2020; Received IBM's Pat Goldberg Memorial Best Paper Awards )

A. Burrello, K. Schindler, L. Benini, A. Rahimi, ' Hyperdimensional computing with local binary pat terns: one-shot learning of seizure onset and identification of ictogenic brain regions using short-time iEEG recordings ,” IEEE Transactions on Biomedical Engineering (TBME) , 2020.

A. Rahimi, P. Kanerva, L. Benini, J. M. Rabaey, ' Efficient biosignal processing using hyperdimensionalcomputing: network templates for combined learning and classification of ExG signals ', Proceedings of the IEEE , 2018.

A. Rahimi, S. Datta, D. Kleyko, E. P. Frady, B. Olshausen, P. Kanerva, J. M. Rabaey, ' High-dimensional c omputing as a nanoscalable paradigm ', IEEE Transactions on Circuits and Systems (TCAS-I) , 2017.

A. Rahimi, P. Kanerva, J. M. Rabaey, ' A robust and energy-efficient classifier using brain-inspired hyperdimensional computing ', International Symposium on Low Power Electronics and Design (ISLPED) , 2016.

Research in the News:

2023's Biggest Breakthroughs in Computer Science

A New Approach to Computation Reimagines Artificial Intelligence

Disentangling visual concepts by embracing stochastic in-memory computing

Mimicking the brain: Deep learning meets vector-symbolic AI

The best of both worlds: Deep learning meets vector-symbolic architectures

High-five or thumbs-up? New device detects which hand gesture you want to make

Fulfilling Brain-inspired Hyperdimensional Computing with In-memory Computing

Publications

Mimonets: multiple-input-multiple-output neural networks exploiting computation in superposition.

• Nicolas Menet
• Michael Hersche
• NeurIPS 2023

TCNCA: Temporal Convolution Network with Chunked Attention for Scalable Sequence Processing

• Aleksandar Terzic

Probabilistic Abduction for Visual Abstract Reasoning via Learning Rules in Vector-symbolic Architectures

• Francesco Di Stefano

A Neuro-Vector-Symbolic Architecture for Data- and Compute-Efficient Continual Learning, Abstract Reasoning, and Combinatorial Inference

• ES Week 2023

VSA-based positional encoding can replace recurrent networks in emergent symbol binding

Francesco carzaniga, decoding superpositions of bound symbols represented by distributed representations.

• Zuzanna Opala

Solving Raven's Progressive Matrices via a Neuro-vector-symbolic Architecture

• Mustafa Zeqiri

Generalized Key-Value Memory to Flexibly Adjust Redundancy in Memory-Augmented Networks

• Denis Kleyko

Kumudu Geethan Karunaratne

• 07 Feb 2022

Device For High Dimensional Encoding

• 17 Jan 2022

Neuro-Vector-Symbolic Architecture

• Neuro-symbolic AI
• Foundation Models
• Knowledge and Reasoning
• AI Hardware
• Computer Vision
• Exploratory Science

In-memory computing

• Physical Sciences

Disentangling visual attributes with neuro-vector-symbolic architectures, in-memory computing, and device noise

This ai could likely beat you at an iq test.

In-memory physical superposition meets few-shot continual learning

Top collaborators

Abu Sebastian

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IBM Research

From iis-projects.

• 1.1 About the IBM Research–Zurich
• 2.1 Useful Reading
• 2.2 Prerequisites
• 3 Available Projects

Short Description

Today, we are entering the era of cognitive computing, which holds great promise in deriving intelligence and knowledge from huge volumes of data. In today’s computers based on von Neumann architecture, huge amounts of data need to be shuttled back and forth at high speeds, a task at which this architecture is inefficient.

It is becoming increasingly clear that to build efficient cognitive computers, we need to transition to non-von Neumann architectures in which memory and processing coexist in some form. At IBM Research–Zurich in the Neuromorphic and In-memory Computing Group , we explore various such computing paradigms from in-memory computing to brain-inspired neuromorphic computing. Our research spans from devices and architectures to algorithms and applications.

About the IBM Research–Zurich

The location in Zurich is one of IBM’s 12 global research labs. IBM has maintained a research laboratory in Switzerland since 1956. As the first European branch of IBM Research, the mission of the Zurich Lab, in addition to pursuing cutting-edge research for tomorrow’s information technology, is to cultivate close relationships with academic and industrial partners, be one of the premier places to work for world-class researchers, to promote women in IT and science, and to help drive Europe’s innovation agenda. Download factsheet

Hybrid AI Systems (HAS)

Neither symbolic AI nor neural networks alone has produced the kind of intelligence expressed in human and animal behavior. Why? Each has a long and rich history, but has addressed a relatively narrow aspect of the problem. Symbolic AI focuses on solving cognitive problems, drawing upon the rich framework of symbolic computation to manipulate internal representations in order to perform reasoning and inference. But it suffers from being non-adaptive, lacking the ability to learn from example or by direct observation of the world (aka symbol grounding problem). Neural networks on the other hand have the ability to learn from data, and derive much of their power from nonlinear function approximation combined with stochastic gradient descent. But intelligence requires more than modeling input-output relationships. Without the richness of symbolic computation, neural nets lack the simple but powerful operations such as variable binding that allow for analogy making and reasoning, which underlie the ability to generalize from few examples. To address this gap, neurosymbolic AI aims to combine the best of both worlds to approach human-level intelligence.

We approach the problem from a very different perspective, inspired by the brain’s high-dimensional circuits and the unique mathematical properties of high-dimensional spaces. It leads us to a novel information processing architecture that combines the strengths of symbolic AI and neural networks, and yet has novel emergent properties of its own. By combining a small set of basic operations on high-dimensional vectors, we obtain hybrid AI system (HAS) that makes it possible to represent and manipulate data in ways familiar to us from symbolic AI, and to learn from the statistics of data in ways familiar to us from artificial neural networks and deep learning. Further, principles of such HAS allow few-shot learning capabilities, and extremely robust operations against failures, defects, variations, and noise, all of which are complementary to ultra-low energy computation on nanoscale fabrics such as phase-change memory devices. Exciting further research (listed in below table) awaiting in this direction spans high-level algorithmic exploration all the way to efficient hardware design for emerging computational fabrics.

Useful Reading

• Neurosymbolic AI Explained, IBM-Research
• Neurosymbolic AI, Invited Talk by David Cox, IAAI /AAAI 2020
• In-memory hyperdimensional computing , Nature Electronics, 2020.
• Robust high-dimensional memory-augmented neural networks , Nature Communications, 2021.
• In-memory factorization of holographic perceptual representations , Nature Nanotechnology, 2023.

Prerequisites

• Background in machine learning ( recommended )
• Experience with any deep learning framework such as TensorFlow or PyTorch ( recommended )
• VLSI I ( recommended )

Available Projects

We are inviting applications from students to conduct their thesis (bachelor, semester, and master) or an internship project at the IBM Research lab in Zurich on this exciting new topic.

• Semester Thesis
• Master Thesis

Johannes “Georg” Bednorz was a fresh-faced earth sciences student in 1972 when he spent three months working at IBM’s research laboratory in Zurich. The summer break from undergraduate studies turned out to be an important inflection point. It’s when he met Alex Müller . A decade later, the two men would begin collaborating on pioneering materials research that continues to shape our modern world.

Together they made seminal advancements in the study of high-temperature superconductors, often referred to as HTS, discovering materials with the ability to radically improve the power and efficiency of electrical transmission. Their work opened the door to numerous commercial applications — from magnetic resonance imaging (MRI) and high-speed rail to lighter, smaller wind turbines and more efficient smart energy grids.

The duo’s research garnered them the Nobel Prize in Physics in 1987.

Bednorz was born in Neuenkirchen in North Rhine-Westphalia, Germany, in 1950, the youngest of four children. He was raised by Anton Bednorz, an elementary school teacher, and Elisabeth, a piano teacher, both of whom had fled Central Europe during World War II. Bednorz recollected on his upbringing in his autobiography, which was published in the Nobel Lectures : “My parents, originating in Silesia, had lost sight of each other during the turbulences of World War II, when my sister and two brothers had to leave home and were moved westwards. I was a latecomer, completing our family after its joyous reunion in 1949.”

Bednorz’s parents nudged their son to pursue classical music, but he harbored a greater passion for motorcycle and automobile mechanics. In high school, he developed an interest in natural sciences, primarily chemistry, but his mentors also encouraged him to appreciate the humanities. “At school,” he noted, “it was our teacher of arts who cultivated that practical sense and helped to develop creativity and team spirit within the class community, inspiring us to theater and artistic performance even outside school hours.”

Bednorz enrolled at the University of Münster in 1968 to study chemistry but shifted to crystallography, a subfield of mineralogy that blends chemistry and physics. His teachers arranged his summer internship at IBM in 1972, which led to subsequent periods with the company during graduate studies. “I soon was impressed by the freedom even I as a student was given to work on my own, learning from mistakes and thus losing the fear of approaching new problems in my own way,” he wrote. His future wife, Mechthild Wennemer, joined him in a move to Zurich, where they both pursued their doctorates. “Since then,” he said, “she has acted as a stabilizing element in my life and is the best adviser for all decisions I make, sharing the ups and downs in an unselfish way.”

Bednorz earned his doctorate in 1982 from the Swiss Federal Institute of Technology, under the tutelage of Müller and Professor Heini Gränicher. The same year, Müller, then director of the lab’s physics division, brought him on staff at the IBM Zurich Research Laboratory.

Grasping the accomplishments of Bednorz and Müller requires first understanding a bit of foundational superconductivity research. In 1911, Dutch scientist Heike Kamerlingh Onnes had discovered a process thought to be the closest approximation to a naturally occurring perpetual motion machine. He found that superconductivity, or zero electrical resistance, could be achievable when certain alloys were cooled close to absolute zero — in his case, 4.19 Kelvin for liquid mercury. (Absolute zero is 0 Kelvin (K), -273 degrees Celsius (C), or -459 degrees Fahrenheit (F).) In subsequent decades, superconductivity was found in lead at 7 K, in niobium at 10 K, and in niobium nitride at 16 K.

For the next three-quarters of a century, researchers made little progress in their hunt for compounds that could serve as superconductors at more practical temperatures. Their best effort peaked at 23 K with another niobium-based material.

The return of Bednorz to IBM, and an intense collaboration with Müller, greatly accelerated progress. The duo focused on a class of oxides known as perovskites. To obtain a chemically stable material, they added barium to crystals of lanthanum-copper-oxide to produce a ceramic. It eventually became the first HTS. The revelation was initially greeted with skepticism because ceramics were generally considered insulators, not conductors. But the new material withstood repeated tests to demonstrate superconductivity at 35 K.

It was a significant finding because 35 K requires far less cooling with liquid helium (4.2 K), a very limited resource. It also represented a significant leap toward 77 K. At that point, superconductors can be cooled with liquid nitrogen, which it’s possible to condense from air using refrigeration techniques — making the entire process easier and less expensive. The discovery opened a whole field of research into what is considered something of a holy grail in physics: room-temperature superconductivity.

In January 1986, Müller and Bednorz’s discovery unleashed a flurry of activity among physicists who imagined new applications in electrotechnology and microelectronics. “We both realized the importance of our discovery,” recalled Bednorz, “but were surprised by the dramatic development and changes in both the field of science and our personal lives.” Within a year, several groups had prepared their own versions of the IBM compound and reported similar results. By March 1987, thousands of scientists and engineers were researching other versions of the new class of oxide superconductors in hopes of unlocking more applications.

Before long, scientists had found materials that achieved superconductivity at 77 K — the key threshold for using liquid nitrogen. “This discovery is quite recent, less than two years old,” said Gösta Ekspong of the Royal Swedish Academy of Sciences in late 1987, “but it has already stimulated research and development throughout the world to an unprecedented extent.”

The fevered activity peaked at the March 1987 American Physical Society meeting in New York. Dubbed the “Woodstock of Physics,” the event counted more than 50 scientists presenting discoveries that achieved dramatically higher superconductivity temperatures than ever before. Müller and Bednorz were honored that same year with the Nobel Prize. It was the shortest elapsed time ever between a discovery and the award for any scientific Nobel. Bednorz was subsequently named an IBM Fellow, the company’s highest technical achievement.

The quest to fully harness the potential of high-temperature superconductors continues, with a primary focus on electric power transmission, high-speed rail and other novel modes of frictionless transportation such as magnetic levitation trains. In 2020, a team of physicists in New York published research about a novel compound of hydrogen, carbon and sulfur that, when compressed to extreme pressure, operates as a superconductor at up to 59 degrees Fahrenheit. The next step is to discover a compound that behaves similarly under normal atmospheric pressure.

Meanwhile, scientists and engineers are testing how high-temperature semiconductors can improve the energy efficiency of power cables. Nearly every hospital now employs magnetic resonance imaging scanners (commonly known as MRIs) using small superconducting coils to produce a rotating magnetic field that creates detailed images of the human body. Some countries are even testing trains that use onboard magnets to levitate vehicles above steel rails, potentially making trains much faster and more efficient.

“HTS will impact all aspects of energy infrastructure,” said Bednorz, who is now retired, in the journal Nature Reviews Materials . “HTS has the potential to develop into the key technology of the 21st century.”

The IBM researcher shared a Nobel Prize in Physics in the 1980s. It was just the beginning of his legacy.

IBM’s Nobel Prize-winning work on energy efficiency promises to unlock innovations across industry and society

IBM has always provided its scientists the freedom to explore and discover solutions to society's greatest problems

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What's the secret to Switzerland's status as a global talent hub?

Switzerland is a powerhouse in attracting multinationals and creating, attracting and retaining individual talents. Image:  PhoHenrique Ferreira on Unsplash

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Stay up to date:, jobs and skills.

• Switzerland has once again been named the world’s most talent-competitive country, according to two different reports.
• This pristine Alpine country offers a high quality of life, including excellent education and employment prospects.
• What can other countries learn from Switzerland about becoming a global talent hub?

Another year, another occasion for Switzerland to shine when it comes to top talent. In 2023, this tiny Alpine country ranked first, again, in two global reports – The Global Talent Competitiveness Index 2023 and the IMD World Talent Ranking . It also ranked first in quality of life, according to the most recent Human Development Report from the UN, and managed to retain first place for the 13th consecutive year when it comes to innovation, according to the World Intellectual Property Organization (WIPO) 2023 Innovation Index.

It is the high quality of life in Switzerland that helps to attract – and retain – top talent. And the talent creates innovation - which powers industry and economy, in a synergistic loop creating value for everybody and thus the high quality of life.

Yes, there is nature – the picturesque Alps, clear lakes and postcard-pretty hills with grazing cows. But this nature wouldn’t have stayed this pristine, and the air wouldn’t have been that clean, were it not for the government's efforts, decade after decade, ensuring the country’s economic development in parallel with environmental conservation and commitment to sustainability.

Have you read?

Skills first: 6 success factors for recruiting and developing talent, growth and jobs at davos 2024: what to know, adopting a 'skills-first' approach could help more than 100 million people worldwide get better jobs.

These aspects attract top global talent as well as multinationals, drawn by the business-friendly culture of political stability and legal transparency, commitment to intellectual property protection and free-market principles. Global companies establish their presence here and tap into the vast pool of professionals, enabling them to develop further and, in a synergistic way, contribute to Swiss society and to Europe. As for domestic companies, the country’s strong export business focus is the driving force when it comes to innovation, ensuring that they are competitive globally.

Investing in and nurturing future talent

Overall, it’s been a strategic combination of policies. A stable economy is one; then, a world-class education system and mandatory health insurance enable access to top-notch medical services.

In addition to paying attention to sustainability, the Swiss are also very conscious of work-life balance and value their personal time, as well as adequate social support and emphasis on safety and security. Add to the mix the importance of diversity and inclusion nationwide, and you’ve got great conditions for life satisfaction and professional growth.

Kids growing up here have access to top-quality education pretty much from the very beginning in a system that allows everyone to grow depending on their individual talents. When they are around 12, they may opt to apply to gymnasium, or Gymi – the academic track of secondary education that gets pupils ready for university. Another option is the unique vocational education and training (VET). Both approaches are excellent for the development of young talent. While Gymi places a lot of focus on intense schoolwork, the VET is a dual-track system that allows teens to combine classroom learning and internships as early as the age of 14.

When they turn 15, they can decide to do a full-time, year-long apprenticeship, a mix of on-the-job training and theoretical studies at a vocational school. Such early intro to the working world allows young people to enter the workforce seamlessly – and after three to four years, they can still apply to university if they wish.

There are a lot of universities to choose from. Switzerland has several world-class, top-ranking universities, including ETH and EPFL, as well as research institutions. Strong emphasis on science, technology, engineering, and mathematics (STEM) related subjects helps this nation nurture highly skilled professionals. Once they graduate, young people enter a job market that offers robust infrastructure and the commitment of the Swiss government to innovation and research in general. Investment in research and development (R&D) is highly prioritised, leading to an an innovation-driven environment where creativity and collaboration thrive.

Collaboration between industry and academia

Collaboration is encouraged widely – between academia and industry, be it large companies, startups or spinoffs. Collaboration starts even before students graduate. For example, multinationals like IBM Research and others have programmes that see Master’s and PhD students get degrees following a dual academia-industry system. They dip their toes into the research culture of an industrial organization while still students and may end up on top of the ladder should a vacancy appear at the company when they graduate.

Programmes like these further amplify the "brain gain" of skilled graduates and established workers from abroad. For over half a century, Zurich Lab, part of IBM Research, has trained thousands of young people via partnerships with local universities. Many have gone on to win awards and lead projects on groundbreaking technologies. Nearly every year, one or more young people at the lab get nominated for MIT's prestigious "35 under 35" Innovators Award. Alain Vaucher , for example, a computational chemist who developed an AI system that puts together a chemical recipe for any molecule, won the award in 2022.

In a synergistic loop, the top-level experience young people get helps multinationals increase contributions to society – in Switzerland and overall in Europe. And it prompts more global companies to come to the country and tap into this vast reserve of highly skilled professionals. They come and start to appreciate the great work-life balance the Swiss culture offers – and they stay, in the process, helping to solidify the position of this Alpine country as a powerhouse in attracting multinationals and creating, attracting and retaining individual talents.

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Protecting art and passwords with biochemistry

Security experts fear Q-​Day, the day when quantum computers become so powerful that they can crack today's passwords. Some experts estimate that this day will come within the next ten years. Password checks are based on cryptographic one-​way functions, which calculate an output value from an input value. This makes it possible to check the validity of a password without transmitting the password itself: the one-​way function converts the password into an output value that can then be used to check its validity in, say, online banking. What makes one-​way functions special is that it's impossible to use their output value to deduce the input value -- in other words, the password. At least not with today's resources. However, future quantum computers could make this kind of inverse calculation easier.

Researchers at ETH Zurich have now presented a cryptographic one-​way function that works differently from today's and will also be secure in the future. Rather than processing the data using arithmetic operations, it is stored as a sequence of nucleotides -- the chemical building blocks of DNA.

Based on true randomness

"Our system is based on true randomness. The input and output values are physically linked, and it's only possible to get from the input value to the output value, not the other way round," explains Robert Grass, a professor in the Department of Chemistry and Applied Biosciences. "Since it's a physical system and not a digital one, it can't be decoded by an algorithm, not even by one that runs on a quantum computer," adds Anne Lüscher, a doctoral student in Grass's group. She is the lead author of the paper, which was published in the journal Nature Communications .

The researchers' new system can serve as a counterfeit-​proof way of certifying the authenticity of valuable objects such as works of art. The technology could also be used to trace raw materials and industrial products.

How it works

The new biochemical one-​way function is based on a pool of one hundred million different DNA molecules. Each of the molecules contains two segments featuring a random sequence of nucleotides: one segment for the input value and one for the output value. There are several hundred identical copies of each of these DNA molecules in the pool, and the pool can also be divided into several pools; these are identical because they contain the same random DNA molecules. The pools can be located in different places, or they can be built into objects.

Anyone in possession of this DNA pool holds the security system's lock. The polymerase chain reaction (PCR) can be used to test a key, or input value, which takes the form of a short sequence of nucleotides. During the PCR, this key searches the pool of hundreds of millions of DNA molecules for the molecule with the matching input value, and the PCR then amplifies the output value located on the same molecule. DNA sequencing is used to make the output value readable.

At first glance, the principle seems complicated. "However, producing DNA molecules with built-​in randomness is cheap and easy," Grass says. The production costs for a DNA pool that can be divided up in this way are less than 1 Swiss franc. Using DNA sequencing to read out the output value is more time-​consuming and expensive, but many biology laboratories already possess the necessary equipment.

Securing valuable goods and supply chains

ETH Zurich has applied for a patent on this new technology. The researchers now want to optimise and refine it to bring it to market. Because using the method calls for specialised laboratory infrastructure, the scientists think the most likely application for this form of password verification is currently for highly sensitive goods or for access to buildings with restricted access. This technology won't be an option for the broader public to check passwords until DNA sequencing in particular becomes easier.

A little more thought has already gone into the idea of using the technology for the forgery-​proof certification of works of art. For instance, if there are ten copies of a picture, the artist can mark them all with the DNA pool -- perhaps by mixing the DNA into the paint, spraying it onto the picture or applying it to a specific spot.

If several owners later wish to have the authenticity of these artworks confirmed, they can get together, agree on a key (i.e. an input value) and carry out the DNA test. All the copies for which the test produces the same output value will have been proven genuine. The new technology could also be used to link crypto-​assets such as NFTs, which exist only in the digital world, to an object and thus to the physical world.

Furthermore, it would support counterfeit-​proof tracking along supply chains of industrial goods or raw materials. "The aviation industry, for example, has to be able to provide complete proof that it uses only original components. Our technology can guarantee traceability," Grass says. In addition, the method could be used to label the authenticity of original medicines or cosmetics.

• Organic Chemistry
• Telecommunications
• Biochemistry
• Computers and Internet
• Information Technology
• Mathematical induction
• Quantum computer
• Linus Pauling
• Three-phase electric power
• Quantum number
• Quantum entanglement
• Euclidean geometry

Story Source:

Materials provided by ETH Zurich . Original written by Fabio Bergamin. Note: Content may be edited for style and length.

Journal Reference :

• Anne M. Luescher, Andreas L. Gimpel, Wendelin J. Stark, Reinhard Heckel, Robert N. Grass. Chemical unclonable functions based on operable random DNA pools . Nature Communications , 2024; 15 (1) DOI: 10.1038/s41467-024-47187-7

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