SciTransfer
Organization

EIDGENOESSISCHE TECHNISCHE HOCHSCHULE ZUERICH

Switzerland's leading technical university with deep strengths in machine learning, climate science, advanced materials, and high-performance computing across 596 H2020 projects.

University research groupmultidisciplinaryCH
H2020 projects
596
As coordinator
256
Total EC funding
€433.5M
Unique partners
2788
What they do

Their core work

ETH Zürich is Switzerland's premier technical university and one of Europe's top research institutions, operating across virtually every domain of science and engineering. Their H2020 portfolio spans machine learning, climate science, advanced materials, quantum technologies, high-performance computing, and life sciences — reflecting a university where fundamental research meets applied problem-solving. They generate both foundational knowledge (through a massive ERC and MSCA portfolio) and contribute domain expertise to large collaborative projects in energy, environment, health, and digital technologies. With over EUR 433 million in H2020 funding across 596 projects, they function as a research powerhouse that other institutions build consortia around.

Core expertise

What they specialise in

15 projects

Machine learning appears as the top keyword across both early and recent periods (6→9 projects), alongside deep learning and big data, spanning applications from climate to health.

Climate and Earth system scienceprimary
24 projects

Climate change, earth system models, climate mitigation, adaptation, and storm tracks feature prominently across the Environment sector (24 projects) with projects like ESMERALDA and ENVRI PLUS.

12 projects

HPC and exascale computing are consistent keywords across both periods, supported by participation in PRACE-4IP and Research Infrastructure projects (12 projects).

10 projects

Metamaterials (4 recent projects), graphene, catalysis, and electrodeposited alloys feature in projects like SELECTA, with growing emphasis in the recent period.

Microfluidics and bioengineeringemerging
7 projects

Microfluidics surged to 7 mentions in the recent period (from near-zero earlier), signaling a rapidly growing capability alongside microbiome research (4 recent projects).

6 projects

Quantum simulation featured in early-period work (projects RYSQ, QUIC), with neuromorphic computing appearing in recent keywords, showing continued investment in next-generation computing.

Evolution & trajectory

How they've shifted over time

Early focus
Big data and quantum simulation
Recent focus
Microfluidics and climate modeling

In the early H2020 period (2015–2018), ETH Zürich focused on big data analytics, quantum simulation, synthetic biology, and career development through MSCA training networks. By the later period (2019–2022), a clear shift emerged toward applied and physical sciences — microfluidics surged as a new strength, earth system models and climate mitigation gained prominence, and 3D printing and metamaterials signaled growing materials science ambitions. Machine learning remained a constant thread but deepened, moving from a research topic to an enabling capability applied across domains.

ETH Zürich is shifting from purely computational and theoretical work toward physically grounded research — materials, microfluidics, and climate systems — while using ML as a cross-cutting accelerator.

Collaboration profile

How they like to work

Role: consortium_leaderReach: Global80 countries collaborated

ETH Zürich is both a consortium leader and a sought-after specialist partner — coordinating 256 projects (43%) while participating in 318 others, indicating they are equally comfortable driving research agendas and contributing deep expertise to others' visions. With 2,788 unique consortium partners across 80 countries, they operate as a major European research hub rather than sticking to a small circle of repeat collaborators. Their heavy ERC and MSCA portfolio (157 individual grants) also shows strong capacity for independent, PI-driven research alongside large collaborative efforts.

ETH Zürich has collaborated with 2,788 distinct partner organizations across 80 countries, making it one of the most connected institutions in H2020. Their network spans all of Europe and extends globally, with strong ties across Western European research universities and industrial partners.

Why partner with them

What sets them apart

ETH Zürich combines the breadth of a top-5 European research university with Switzerland's unique position as an associated (non-EU) H2020 country, bringing world-class talent and infrastructure without EU membership constraints. Their rare combination of strength in both computational sciences (ML, HPC, exascale) and experimental physical sciences (microfluidics, metamaterials, catalysis) means they can bridge the gap between simulation and real-world validation within a single institution. For consortium builders, ETH brings not just expertise but credibility — their name on a proposal signals scientific rigor to evaluators.

Notable projects

Highlights from their portfolio

  • Flourish
    ETH coordinated this robotics-meets-agriculture project on aerial data collection for precision farming, showcasing their ability to bridge digital technologies with real-world food sector applications.
  • PRACE-4IP
    Participation in the PRACE HPC infrastructure project positions ETH as a key node in Europe's supercomputing ecosystem, underpinning their computational strengths across all domains.
  • ESMERALDA
    This ecosystem services mapping project demonstrates ETH's environmental policy engagement, connecting ecological modeling directly to European biodiversity strategy and decision-making.
Cross-sector capabilities
Digital — machine learning, HPC, and data science applicable to any domainEnvironment — climate modeling, earth systems, ecosystem servicesHealth — epidemiology, personalized medicine, microbiome researchEnergy — energy efficiency, catalysis, and advanced materials for energy applications
Analysis note: With 596 projects and EUR 433M in funding, ETH Zürich has one of the richest H2020 profiles in the dataset. The 30-project sample shown is heavily weighted toward 2015 starts; the keyword analysis across early/recent periods provides the best signal for evolution. The institution's sheer breadth means any single profile necessarily simplifies — individual departments and labs will have much more focused capabilities.