Search results

Positions: The Gordon Center for Medical Imaging (GCMI) in the Department of Radiology at Massachusetts General Hospital (MGH) and Harvard Medical School (HMS) has multiple openings for highly qualified individuals at the postdoctoral level to work with Prof. Kuang Gong on NIH funded projects related to PET image reconstruction, medical image analysis, and machine learning methodologies.

Section: Events

Date: May 19, 2022 Time: 10:00 AM ET (New York Time) Title: Scalable Algorithms for Distributed Principal Component Analysis Registration | Full webinar details

Section: Events

Date: June 24, 2022 Time: 12:00 PM ET (New York Time) Title: Fairness-aware Learning Over Networks Registration | Full webinar details

Section: Events

Date: January 27, 2022 Time: 10:00 AM ET (New York Time) Title: Signal Processing for THz Communications Registration | Full webinar details

Section: Events

Date: February 24, 2022 Time: 11:00 AM ET (New York Time) Title: Recent Advances of Deep Learning within X-ray Security Imaging Registration | Full webinar details

Section: Events

Date: January 26, 2022 Time: 9:00 AM ET (New York Time) Title: Bayesian Multi-object Tracking: Probability Hypothesis Density Filter and Beyond Registration | Full webinar details

Section: Events

Date: December 8, 2021 Time: 08:20 AM ET (New York Time) Title: Is Machine Learning Security IFS-Business as Usual? Registration | Full webinar details

Section: Events

Date: December 20, 2021 Time: 8:00 AM ET (New York Time) Title: Infinite-Dimensional Expansion for Sound Field Estimation with Application to Spatial Audio Registration | Full webinar details

Section: Events

Date: December 10, 2021 Time: 10:00 AM ET (New York Time) Title: Image Fusion with Convolutional Sparse Representation Registration | Full webinar details

Section: Events

Date: December 9, 2021 Time: 11:00 AM ET (New York Time) Title: Scaling and Scalability: Accelerating Ill-conditioned Low-rank Estimation via Scaled Gradient Descent Registration | Full webinar details