Sort by
Refine Your Search
-
Country
-
Employer
-
Field
-
programme Is the Job related to staff position within a Research Infrastructure? No Offer Description The IMMENSE project is funded by the University of Lille, France. Our overarching goal is to image
-
Radiography: Radiography involves the controlled use of ionising radiation, radioactive substances and non-ionising radiation to produce diagnostic quality images of the human body, facilitating the diagnosis
-
requirements described will be excluded. Preferential factors: Experience in image analysis and processing using AI algorithms; English language proficiency; Portuguese language fluency. APPLICATION
-
Radiography: Radiography involves the controlled use of ionising radiation, radioactive substances and non-ionising radiation to produce diagnostic quality images of the human body, facilitating the diagnosis
-
Max Planck Institute for Multidisciplinary Sciences, Göttingen | Gottingen, Niedersachsen | Germany | 3 months ago
Job Code: 09-25 Job Offer from July 04, 2025 The Max Planck Institute for Multidisciplinary Sciences is a leading international research institute of exceptional scientific breadth. With more than
-
, radioactive substances and non-ionising radiation to produce diagnostic quality images of the human body, facilitating the diagnosis and treatment of patients (Allied Health Professions Act 2011). Dietetics
-
Diseases (NIAID ) National Institute of Arthritis and Musculoskeletal and Skin Diseases (NIAMS ) National Institute of Biomedical Imaging and Bioengineering (NIBIB ) National Institute on Deafness and Other
-
of Biomedical Imaging and Bioengineering (NIBIB ) Eunice Kennedy Shriver National Institute of Child Health and Human Development (NICHD ) National Institute on Deafness and Other Communication Disorders (NIDCD
-
with such environments. We investigate machine learning approaches to infer semantic understanding of real-world scenes and the objects inside them from visual data, including images and depth/3D
-
with such environments. We investigate machine learning approaches to infer semantic understanding of real-world scenes and the objects inside them from visual data, including images and depth/3D