14 Doctoral Candidates in e-health

Click on the text  to discover more about the doctoral project of the 14 fellows of the BoostUrCAreer Programme.

Riccardo Di Dio
Riccardo Di Dio

Riccardo Di Dio

DTBC: Detection Tool for Broncho Constrictions... learn more!

Iryna S
Iryna S

Iryna Shcheholeva

TORNADOS: Targeting of oncogenic microRNAs with small molecules: toward new chemotherapies... learn more!

Santiago S
Santiago S

Santiago Silva

FED-BIONET: Federated learning of biomedical data in large-scale networks of multicentric imaging-genetics information... learn more!

Giulia R
Giulia R

Giulia Rocco

MuExC3Po: Multimodal Functional Exploration of Cerebellar Cognitive Cues and Particularities in Neurology... learn more!

Arnab D
Arnab D

Arnab Dey

BiomechanicsCapture: Deep learning based Real-time Biomechanics Capture... learn more!

Sandra K
Sandra K

Sandra Kovachka

Patched-e-ChemBiol: Inhibition of Patched Chemotherapy Resistance Activity: biocomputational, chemical and cellular approaches... learn more!

Abid A
Abid A

Abid Ali

ACt4autism: Action Detection for Improving the Diagnosis of Autism... learn more!
 
Sakshi K
Sakshi K

Sakshi Khaiwal

PRELUDE : Predicting the evolution of drug resistance... learn more!

Giang Nam P
Giang Nam P

Giang Nam Pham

e-MDR: Development of new antibiotics against clinical multidrug resistant bacteria from untapped marine microorganisms, a chemobiology approach... learn more!

Sahar B
Sahar B

Sahar Borzooei

MEDSCAN: Numerical solution for a hand-held high-resolution medical scanner... learn more!

Hari S
Hari S

Hari Sreedhar, M.D.

Thyrosonics: Machine learning-based detection and classification of thyroid nodules from ultrasound images... learn more!

Nuttanun S
Nuttanun S

Nuttanun Siriaporn

ASTHAIR: Active transport, health, and environment... learn more!

Mehran H
Mehran H

Mehran Hatamzadeh

MoVE-IT: Motion Analysis By Video For Gait Evaluation With Innovative Technology... learn more!

anonymous_m
anonymous_m

Kevin Dsouza

MitoIntegrOMICS : an integrated multi-OMICS approach to increase the diagnostic power for mitochondrial diseases