computer and information sciences

PRIMAL · Private Machine Learning

MLending · An ML Approach to IFIs: Determinants of IMF Lending

EM-PRIOR · Single Particle Cryo-EM Reconstruction with Convolutional Neural Networks

MOAI · MethOds And Instrumentation for laser guide star wavefront sensing

RENE · Regional Religious Networks in the Roman Empire

ExpSoundscapes · Experiencing Historical Soundscapes: the Royal Entries of Emperor Charles V in Iberian Cities

TAXGLOBAL · Understanding the global effect of corporate tax reforms

SYNPARK · Multimodal imaging in parkinsonisms: from the molecular synaptic pruning to the whole-brain connectomics

2 partners · 2 countries

ICOME · Individualised COgnitive and Motor learning for the Elderly

FLOW · Our elemental sense of collective flow

LanguageOfDNA · Deciphering the Language of DNA to Identify Regulatory Elements and Classify Transcripts Into Functional Classes

DeepFIN · Assessing Archaeological Deep Time in Finland through Spatial Exploration 500 BCE - 1520 CE

H-3D-SOC · 3D IC Design Flow for Hybrid-bonding 3D System on Chip

ESNECO · Estimation of Neural Code from the Electroencephalogram (EEG)

TheDarkNeutrino · Search for the dark neutrino at the NA62 experiment at CERN

BNNmetrics · Bayesian Neural Networks for Bridging the Gap Between Machine Learning and Econometrics

MALOT · Managing Mobility Data Quality for Location of Things

CYCLOCARB · Oceanic Carbon Cycling Response to Global Temperature Changes

SmartDeZIgn · Smart Design Tool of High Performing ZIF Membranes for Important CO2-Related Separations

ID-earlyMCI · Objective home-based EEG prediction of aMCI: Identification of a predictive electrophysiological model of cognitive function in amnesic mild cognitive impairment.

PIGGY · Reasoning using tangles on parity games

EXCHANGE_inLCs · EXamining CHemistry and Nanoparticle Geometry Effects at the INterface of Liquid CrystalS

REFUDEPRE · Addressing Postpartum Depression in Refugees: Impact on Infants and the Role of Home Visiting Programs

IT-DNN · INFORMATION-THEORETIC LIMITS FOR DEEP NEURAL NETWORKS