| KEYWORD |
Machine Learning for Diagnosing SARS/CoV2 from the Analysis of Patients' Exhalations
Thesis in external company
keywords COVID-19, DEEP LEARNING, MACHINE LEARNING, ARTIFICIAL NEURAL NETWORKS, SARS/COV2
Reference persons GIOVANNI SQUILLERO
External reference persons Raffaele Correale (NanoTech Analysis S.r.l.)
Research Groups DAUIN - GR-05 - ELECTRONIC CAD & RELIABILITY GROUP - CAD
Thesis type RESEARCH / EXPERIMENTAL
Description 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.
Notes Send a CV to squillero@polito.it
Deadline 31/12/2021
PROPONI LA TUA CANDIDATURA