JAE Intro ICU AIHUB 2021

Se convocan 10 becas de introducción a la investigación “JAE Intro ICU”, tres de ellas en el IFISC (CSIC-UIB). La convocatoria permanecerá abierta del 5 de octubre hasta el 25 de octubre, ambos incluídos. Las becas JAE Intro #JAEIntroCONEXIONES están asociadas a la denominada red “CONEXIÓN-CSIC en Inteligencia Artificial (AIHUB)”. La CONEXIÓN AIHUB está formada por una red de grupos de investigación del CSIC dedicados a la investigación básica en Inteligencia Artificial o en la aplicación de la Inteligencia Artificial a diferentes dominios científicos.

Más información y formulario de aplicación en:

Los tres proyectos asociados al IFISC son:

aIdentificador del proyecto: JAEIntroAIHUB21-09
Tutor: Massimiliano Zanin
Título del proyecto: Modular deep learning architectures for the classification of time series
Resumen del proyecto: Within the larger field of Deep Learning, many models have been proposed to tackle the problem of classifying time series – e.g. to be able to detect the health condition of a patient given a time series representing some bodily observable. These are usually adapted from other problems, mostly image classification, and some of the most popular and effective solutions include Fully Convolutional Neural Network (FCN), Multi-Channel Deep Convolutional Neural Networks (MCDCNN) or Residual network (ResNet). Notably, these models are based on different configurations of a small set of basic computational elements, e.g. convolutional or pooling layers. In this project we will develop a modular software library, able to train and evaluate different neural network topological structures, with the aim of 1) evaluate to what degree the performance of the model depends on the topology, and 2) whether the best topology is a function of the data under analysis. The evaluation will leverage on multiple data sets, coming from biological (brain dynamics), social (financial markets) and technological (air transport) systems. Finally, the student will have the possibility of working and getting proficient with industry-standard software libraries (TensorFlow and Keras for Python) and hardware infrastructure (in-house cluster of Nvidia GPUs).
Ficha del proyecto: JAEIntroAIHUB21-09

Identificador del proyecto: JAEIntroAIHUB21-19
Tutores: Miguel C. Soriano y Roberta Zambrini
Título del proyecto: Quantum machine learning in the cloud
Resumen del proyecto: Quantum systems are likely to provide a computational advantage over classical systems for machine learning tasks. Currently, the most advanced hardware for quantum computing can only be operated in a few selected research centers around the world. The IBM quantum experience aims at providing remote access to a platform of superconducting qubits, where users can run their algorithms over the quantum hardware. In this project, we will investigate how to operate the IBM quantum computing platform for the processing of sequential information in the context of the machine-learning paradigm of reservoir computing. Reservoir computing is ideally suited to process time series, for instance to forecast the power demand of the electric grid or the occurrence of an earthquake. While quantum reservoir computing has already proven valuable in numerical simulations, the proper way to operate the quantum hardware remains a challenge [1,2]. The candidate will be able to work in edge applications with a particular attention to the role of quantum information encoding and quantum measurement.
Ficha del proyecto: JAEIntroAIHUB21-19

Identificador del proyecto: JAEIntroAIHUB21-20
Tutores: Gian Luca Giorgi y Roberta Zambrini
Título del proyecto: Assessing the power of variational quantum classifiers and quantum extreme learning machines
Resumen del proyecto: While quantum reservoir computing is especially suited to solve time-dependent tasks, quantum extreme learning machines can be useful in problems as diverse as state preparation, state reconstruction, and also data classification. The main scope of this project is to build a comprehensive framework to benchmark the performance of variational quantum classifiers and quantum extreme learning machines, considering both the standard paradigm of classification of classical data and the classification of purely quantum information, such as entanglement, coherence, etc. We will also study the possibility of implementing both methods in physical systems, exploring different theoretical models and experimental platforms. Ver los antecedente y referencias en la ficha a continuación.

Ficha del proyecto: JAEIntroAIHUB21-20