Sort by
Refine Your Search
-
Listed
-
Country
-
Field
-
and peritoneal carcinomatosis on MRI. State-of-the-art convolutional neural networks (e.g., U-Net–based architectures) will be trained and validated to achieve accurate and reproducible volumetric
-
trustworthy AI architectures in domains such as generative AI, large language models, neural networks, and imaging as well as to integrate various types of data to advance research and improve clinical decision
-
optoelectronics and cryogenic device platforms in the context of artificial neural networks and neuromorphics. Information on the department can be found at: https://qdev.nbi.ku.dk/ Our research Our group conducts
-
(2020) “16p11 Duplication Disrupts Hippocampal-Orbitofrontal-Amygdala Connectivity, Revealing a Neural Circuit Endophenotype for Schizophrenia” Cell Reports, 31, 107536, https://doi.org/10.1016/j.celrep
-
combination of physics-based, and data-driven AI-based approaches employing neural-networks and machine learning, this project will develop and validate a multi-time scale DT concept for advanced condition
-
questions of molecular mechanisms, network and cell function, regulation and organization, with possible applications in precision health and microbial ecology. The research could also address advanced data
-
Howard Hughes Medical Institute - Janelia Research Campus | Virginia Beach, Virginia | United States | about 6 hours ago
skills in Python and PyTorch are required, along with the ability to reason about neural network behavior from first principles. We seek candidates who can think critically about model design, understand
-
interdisciplinary areas. Research fields of particular interest include, but not limited to: biomedical science and engineering veterinary science computer science and data science neuroscience and neural
-
neural networks inspired by the human brain and examine what mechanisms enable the networks to acquire human-like intelligence. For more information, please visit our lab homepage. We are currently seeking
-
learning to reconstruct perceived information from functional MRI in real time. For more information on Professor Norman’s lab, see https://compmem.princeton.edu. Questions should be directed to Professor