Data Science Projects
Our Collaborative Data Science Projects represent the diversity of the research and innovation undertaken in data science and AI within the ETH Domain, with scientific partners and with private and public sector stakeholders.
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Canton de Vaud: ENERBAT
Completed
Data-Driven Pathways to Net Zero for the Canton of Vaud’s Building Portfolio
Energy & Sustainability
Climate & Environment
Public sector
Xelidoni: TREMA
Completed
Transforming real estate management with AI
Engineering
Private sector
IRMA
Completed
Interpretable and Robust Machine Learning for Mobility Analysis
Engineering
Academia
FLBI
In Progress
Feature Learning for Bayesian Inference
Physics
Academia
VOCIM
In Progress
Directed Imitation During Vocal Learning
Biology
Academia
Robotoddler
In Progress
Autonomous Robotic Assemblies
Engineering
Academia
ML-Spock
Completed
Machine Learning Supported System for Performance Assessment of Steel Structures Under Extreme Operating Conditions and Management of Risk
Engineering
Academia
CHEMSPEC
Completed
Cost-effective chemical speciation monitoring of particulate matter air pollution
Climate & Environment
Chemistry
Academia
LEAP
Completed
LEArning to Print – towards data-driven real-time predictions for additive manufacturing
Engineering
Academia
FastPoints2Mesh
In Progress
Data-Driven Inference of Mesh-based Representations for Deformable Objects from Unstructured Point Clouds
Engineering
Academia
PAIRED-HYDRO
Completed
Machine learning for the components fatigue prediction in hydropower generation
Energy & Sustainability
Engineering
Academia
DAAAD_Bridges
Completed
Domain-aware-AI Augmented Design of Bridge Structures
Engineering
Academia
DDForms
Completed
Data driven fast optimization of resonant metamaterial structures
Engineering
Academia
SEMIRAMIS
Completed
AI-augmented Architectural Design
Engineering
Academia
4D-Brains
Completed
Extracting Activity from Large 4D Whole-Brain Image Datasets
Biology
Academia
SMARTAIR
Completed
Self-guided Machine Learning Algorithms for Real-Time Assimilation, Interpolation and Rendering of Flow Data
Engineering
Academia
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Our team of data scientists and professionals looks forward to engaging in impactful, results-driven collaborations with you.