-
to demonstrate documented proficiency in English. You have knowledge and expertise in computer vision and/or medical image analysis, deep learning as well as mathematics. You have substantial expertise in
-
releases, and greenhouse gas flux estimation. Current approaches struggle to assimilate data from heterogeneous sensor networks, are too computationally demanding for real-time deployment, and lack reliable
-
, where AI models are trained without having all data in a single computer. This makes it possible to use larger datasets for training, without sending sensitive data between hospitals. The goal is to
-
phenomena. Knowledge and better understanding of solutions to these problems (approximate or otherwise) is of utmost importance for modern industry, science, medicine, and society. As such, the numerical
-
at estimating and optimizing complexity of algorithms. Experience in telecommunications and experience of programming in Python are desirable. The applicant should furthermore have a strong drive towards solving
-
different backgrounds. This position requires that you have graduated at Master’s level in in computer science, media technology, computer engineering, human-computer interaction, visual learning and
-
formally based at the Division of Statistics and Machine Learning (STIMA) within the Department of Computer and Information Science . At STIMA, we conduct research and education in both statistics and
-
workplace The position is formally based at the Division of Statistics and Machine Learning (STIMA) within the Department of Computer and Information Science . At STIMA we conduct research and education in
-
Innovative city . The position is formally based at the Division of Statistics and Machine Learning (STIMA) within the Department of Computer and Information Science . At STIMA, we conduct research and
-
) within the Department of Computer and Information Science . At STIMA we conduct research and education in both statistics and machine learning, at the undergraduate, advanced and PhD levels. We regularly