JAE Intro ICU AIHUB 2021

jae-intro-ai-2021


10 introductory research grants "JAE Intro ICU" are called, three of them at IFISC (CSIC-UIB). The call will remain open from 5 October to 25 October, both included. The JAE Intro #JAEIntroCONEXIONES grants are associated with the so-called "CONEXIÓN-CSIC en Inteligencia Artificial (AIHUB)" network. The AIHUB CONEXIÓN is formed by a network of CSIC research groups dedicated to basic research in Artificial Intelligence or in the application of Artificial Intelligence to different scientific domains.


More information and application form at:  https://www.iiia.csic.es/en-us/education/aihub/becas-jae-intro/


The three projects associated with IFISC are:


Project Identifier: JAEIntroAIHUB21-09

Tutor: Massimiliano Zanin

Project title: Modular deep learning architectures for time series classification

Abstract: Within the broad field of Deep Learning, many models have been proposed to address the problem of time series classification - for example, to be able to detect the health status of a patient given a time series representing some bodily observable. They are often adapted from other problems, especially image classification, and some of the most popular and effective solutions are fully convolutional neural networks (FCN), multi-channel deep convolutional neural networks (MCDCNN) or residual networks (ResNet). In particular, these models are based on different configurations of a small set of basic computational elements, e.g., convolutional or clustering layers. In this project we will develop a modular software library, capable of training and evaluating different topological structures of neural networks, with the aim of 1) evaluating to what extent the model performance depends on the topology, and 2) whether the best topology is a function of the analysed data. The evaluation will be supported by multiple data sets, from biological (brain dynamics), social (financial markets) and technological (air transport) systems. Finally, the student will have the possibility to work with and master industry standard software libraries (TensorFlow and Keras for Python) and hardware infrastructure (proprietary cluster of Nvidia GPUs).

Project ficheJAEIntroAIHUB21-09


Project identifier: JAEIntroAIHUB21-19

Tutors: Miguel C. Soriano and Roberta Zambrini

Project Title: Quantum Machine Learning in the Cloud

Abstract: Quantum systems are likely to provide a computational advantage over classical systems for machine learning tasks. Currently, state-of-the-art hardware for quantum computing can only be operated in a few selected research centres around the world. IBM's quantum expertise aims to provide remote access to a platform of superconducting qubits, where users can run their algorithms on quantum hardware. In this project, we will investigate how to operate IBM's quantum computing platform for sequential information processing in the context of the machine learning paradigm of reservoir computing. Reservoir computing is ideal for processing time series, for example to predict the energy demand of the power grid or the occurrence of an earthquake. Although quantum reservoir computing has already proven its value in numerical simulations, the proper way to operate the quantum hardware remains a challenge [1,2]. The candidate will be able to work on edge applications with a special focus on the role of quantum information encoding and quantum measurement.

Project ficheJAEIntroAIHUB21-19


Project identifier: JAEIntroAIHUB21-20

Tutors: Gian Luca Giorgi and Roberta Zambrini

Project Title: Power evaluation of variational quantum classifiers and quantum extreme learning machines

Abstract: While quantum standby computation is particularly suitable for solving 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 goal of this project is to build a comprehensive framework to evaluate the performance of variational quantum classifiers and quantum extreme learning machines, considering both the standard classical data classification paradigm 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. See the background and references in the fact sheet below.

Project ficheJAEIntroAIHUB21-20

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