Organization

MACHINE2LEARN BV

Dutch AI/ML SME applying machine learning across health, urban data, and brain-inspired computing in large European consortia.

Technology SMEdigitalNLSMEThin data (2/5)

H2020 projects

As coordinator

Total EC funding

€349K

Unique partners

What they do

Their core work

Machine2Learn is a Dutch SME specializing in machine learning and artificial intelligence, applying data-driven algorithms across diverse domains. Their work spans urban data ecosystems (SETA), brain-inspired navigation and mobility technologies (iNavigate), and AI-driven analysis of metabolic and neurological disorders (PRIME). The company acts as a technical AI/ML partner embedded in interdisciplinary research consortia, contributing computational modeling and predictive analytics to projects that require intelligent data processing.

Core expertise

What they specialise in

Machine learning and AI algorithmsprimary

3 projects

Core capability reflected in company name and consistent role across all three projects (SETA, iNavigate, PRIME) spanning different application domains.

Health data analytics for metabolic disorderssecondary

1 project

PRIME project applies ML to insulin multimorbidity including diabetes type 2, obesity, dementia, and Alzheimer's.

Urban data ecosystems and smart mobilitysecondary

2 projects

SETA focused on ubiquitous data ecosystems for urban environments; iNavigate on brain-inspired intelligent navigation and mobility.

Brain-inspired computingemerging

1 project

iNavigate (MSCA-RISE) focuses on brain-inspired technologies for navigation, suggesting growing interest in neuromorphic and bio-inspired AI approaches.

Evolution & trajectory

How they've shifted over time

Early focus

Urban data ecosystems

Recent focus

Health and neuroscience AI

Machine2Learn began with smart city and urban data applications (SETA, 2016), then pivoted toward bio-inspired and health-related AI. Their later projects — iNavigate (2019) on brain-inspired navigation and PRIME (2020) on metabolic multimorbidity — show a clear shift from general data ecosystems toward life sciences and neuroscience-informed computing. The recent keyword profile is entirely health-focused (insulin, diabetes, Alzheimer's, autism), indicating the company is repositioning its ML capabilities toward biomedical applications.

Machine2Learn is migrating its ML expertise from smart city applications toward biomedical and brain-inspired computing, making them an increasingly relevant partner for health and neuroscience consortia.

Collaboration profile

How they like to work

Role: specialist_contributorReach: European16 countries collaborated

Machine2Learn operates exclusively as a participant, never leading consortia — consistent with a specialist SME that contributes technical AI/ML components rather than managing large projects. Despite only three projects, they have worked with 65 unique partners across 16 countries, indicating they integrate into large, diverse consortia rather than small focused teams. This pattern suggests a flexible, low-friction partner that adapts its ML toolkit to whatever domain the consortium requires.

Despite a modest project count, Machine2Learn has built a surprisingly broad network of 65 partners across 16 countries, reflecting participation in large international consortia. Their reach is pan-European with no obvious geographic concentration.

Why partner with them

What sets them apart

Machine2Learn's distinctive value lies in being a domain-agnostic ML company that successfully applies the same core AI competence across radically different fields — from smart cities to Alzheimer's research. For consortium builders, this means a partner who can provide machine learning expertise without needing extensive domain onboarding. Their SME agility combined with experience in large consortia (65 partners) makes them an accessible and adaptable technical contributor.

Notable projects

Highlights from their portfolio

PRIME
Largest-funded project (EUR 160,000) tackling insulin multimorbidity across diabetes, obesity, dementia, and autism — an unusually broad health scope for an ML company.
iNavigate
MSCA-RISE mobility project on brain-inspired navigation technologies, signaling the company's move into neuromorphic and bio-inspired computing.
SETA
Their first H2020 project (EUR 161,242) focused on urban data ecosystems, establishing their credentials in large-scale data processing.

Cross-sector capabilities

Health and biomedical researchSmart cities and urban mobilityNeuroscience and brain-inspired computingPredictive analytics for chronic disease

Analysis note: Only 3 projects with limited keyword data (early-period keywords entirely empty). Company name and project topics strongly suggest ML/AI specialization, but the profile is inferred more from project contexts than from rich descriptive data. No website available for verification. The broad partner network (65 across 16 countries) is notable but likely reflects large consortium sizes rather than intentional network building.