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Membrane for prOtein Separation with designed Architecture of bioInterface at nanosCale – MOSAIC-3D
This project involves a collaboration between the R&D Center for Membrane Technology in Taiwan and the Laboratoire de Génie Chimique in Toulouse (France). It aims at designing a new process for the ultra purification of platelet-derived growth factor (PDGF), a very promising drug which has to be ext
Quantum Experiments with Superconducting Circuits – QuExSuperC
Superconducting quantum circuits with Josephson junctions have shown in the last decade very rich and successful quantum experiments. They clearly appeared as the most promising solid state scalable quantum system for quantum information processor. These superconducting circuits behave as artificial
Domain adaptation from theory to practice – MATTER
Domain Adaptation (DA) is a fundamental problem in statistics, Machine Learning (ML), and data science where one wants to estimate a predictive model from labeled training data in the presence of a shift or change in the properties of the testing data. This problem is very common in practical applic
Online Deep anomaly Detection – ODD
Anomaly detection is a challenge per se. It is unsupervised by nature, as abnormal events are rare, varied, and cumbersome to collect. By exploring deep neural networks for representation learning, the main categories of anomaly detection methods are deep one-class classifiers, autoencoders, generat
Free space Isomorphisms and Isometries – FRII
This project aims at substantially advancing the knowledge about Lipschitz-free spaces and their applications to metric geometry and to functional analysis. For a metric space (M,d) the free space F(M) is a Banach space that is built around the metric space M in such a way that M is isometr
Domain Adaptation for Neural Data Integration – DANDI
Developing machine learning methods suitable for data-limited systems is a core concern of artificial intelligence (AI) for health which typically suffers from sparse data. One promising approach is to leverage related data-rich systems through transfer learning; for example, by pre-training models
Dissipative Evolutions and Convergence to Equilibrium – EVOL
Dissipative evolution equations are key tools for the modeling in Physics, Biology or Mathematics applied to Economics. The study of stationary solutions is not sufficient in most of the cases. As soon as complex dynamics enter into the game or if non-linearities are taken into account, one still ca
Phosphorus and Silicon Heteroles for the Engineering of Molecular Conjugated Materials with Optoelectronic Functions – PSICO
Pi-Conjugated oligomers and polymers based on a planar backbone of sp2-bonded carbon atoms have attracted increasing interest in recent years owing to their potential application for electronic devices. For example, light-emitting diodes (OLED)s for display based on polymer technology are commercial
MAchine learning for environmental TIme Series – MATS
MATS project introduces novel approaches in machine learning for time series, with a specific focus on methods that scale and that can operate even when labelled data is scarce. More specifically, the MATS project aims at introducing new paradigms for large scale time series classification, spatio-
Advanced Monte Carlo Methods for Medical Physics – MoCaMed
The numerical simulation of particle transport in medical physics, known as Monte Carlo Simulation (MCS), is widely used in the research and development of medical imaging systems and radiotherapy treatment systems. However, the growing need for detailed and accurate simulations results in extremely
