Sort by
Refine Your Search
-
Listed
-
Category
-
Country
-
Program
-
Field
-
evaluate innovative methods based on generative models and Vision-Language Models. Design, implement, and validate deep learning approaches for vision applications. Publish research results in leading
-
wellbeing and allows you to be a part of the life of a vibrant and active college campus. To learn more, go to Baylor Benefits & Advantages. Explore & Engage Learn more about Baylor and our strategic vision
-
reproducible analysis workflows Familiarity with computational models of vision and machine learning methods (for example CNNs, deep generative models, encoding models) is preferred but not required Ability
-
. To apply, visit https://northeastern.wd1.myworkdayjobs.com/en-US/careers/job/Boston-MA-Main-Campus/Postdoctoral-Research-Fellow--Moderna---Clinical---Quantitative-Pharmacology_R139074 Copyright 2025
-
computational models of vision and machine learning methods (for example CNNs, deep generative models, encoding models) is preferred but not required Ability to communicate scientific results clearly through
-
algorithms for ultrasound simulation, imaging, and quantitative analysis Customize machine learning / deep learning methods for image reconstruction Conduct human studies of the algorithms and techniques in
-
algorithms for ultrasound simulation, imaging, and quantitative analysis Customize machine learning / deep learning methods for image reconstruction Conduct human studies of the algorithms and techniques in
-
of deep learning in many disciplines, particularly computer vision and image processing. Consequently, coding architectures based on deep learning and end-to-end optimization have been proposed [Ding 2021
-
learning/computer vision methodology. The focus of this project will be on the development of deep learning methodology for spatio-temporal medical image analysis, that is medical images that evolve over
-
AI / Machine learning / Computational Oncology lab:Our work is translationally focused, towards realizing our vision of developing new approaches for fast and low-cost prediction of patient response