| KEYWORD |
Machine Learning for Diagnosing SARS/CoV2 from the Analysis of Patients' Exhalations
Tesi esterna in azienda
Parole chiave COVID-19, DEEP LEARNING, MACHINE LEARNING, ARTIFICIAL NEURAL NETWORKS, SARS/COV2
Riferimenti GIOVANNI SQUILLERO
Riferimenti esterni Raffaele Correale (NanoTech Analysis S.r.l.)
Gruppi di ricerca DAUIN - GR-05 - ELECTRONIC CAD & RELIABILITY GROUP - CAD
Tipo tesi RESEARCH / EXPERIMENTAL
Descrizione NanoTech Analysis is developing innovative nano technologies for performing mass spectrometry. The object of the thesis is the analysis of COVID patients' exhalations, in order to identify appropriate information in the complex mass spectra and correlate the readings to the different cases and specific situations. Preliminary studies are encouraging, and the goal is to build a ML model with a reliability grade (probability of correct identification and data classification) that is acceptable for commercial applications. The candidate will be supported (as needed) by electronic experts, physicians and physicists.
Note Send a CV to squillero@polito.it
Scadenza validita proposta 31/12/2021
PROPONI LA TUA CANDIDATURA