Abstract
Biological tissue discrimination of label-free samples is a non-solved issue. Diagnostics of label-free biological samples could greatly improve the applicability and speed of the process. Advanced phase-sensititive microscopy, together with artificial intelligence algorithms, could contribute to this aim.
Keywords
Biomedical Engineering
artificial intelligence
machine learning
phase-sensitive microscopy
ERC sector(s)
LS Life Sciences
Fields of study
Name supervisor
Felix Fanjul-Velez
E-mail
fanjulf@unican.es
Name of Department/Faculty/School
Electronics Technology, Systems and Automation Engineering-Industrial and Telecommunications Engineering
Name of the host University
University of Cantabria (UC)
EUNICE partner e-mail of destination Research
area.empleo@unican.es
Country
Spain
Thesis level
PhD
Minimal language knowledge requisite
English B2
Spanish B2
Thesis mode
On-site
Start date
Length of the research internship
6 months
Financial support available (other than E+)
Maybe