207 machine-learning "https:" "https:" "https:" "https:" "https:" "Dana Farber Cancer Institute" Postdoctoral research jobs in Denmark
Sort by
Refine Your Search
-
Listed
-
Employer
-
Field
-
in Computer Science, Machine Learning, Artificial Intelligence, Computational Biology, or a closely related field Has strong theoretical and practical experience in deep learning Has hands
-
in electrical engineering, computer engineering, computer science, or similar. Strong background in communication systems, optimization, or machine learning for networked systems. Experience and
-
collaborate with experts in machine learning, immunology and microbiology. You are expected to work independently and coordinate your research with the other team members. Undergraduate research projects will
-
the Machine Learning and Artificial Intelligence. Solid mathematical and analytical skills. Knowledge about statistical machine learning, robotic perception, multimodal AI algorithms. Experience in programming
-
imaging, deep proteomics, metabolomics, metaproteomics, and machine learning (ML) approaches to develop diagnostic classifiers, spatial tissue atlases, and identify potential therapeutic targets
-
is a successful academic community that strives to put knowledge into action by: Engaging partners in the co-creation of knowledge, learning and social change. Empowering our students to become
-
. Software or code development, incl. artificial intelligence and machine learning. Automation and robotics, incl. safe human-machine interaction. Serious gaming, incl. AR/VR. Life cycle analysis. You are
-
imaging, deep proteomics, metabolomics, metaproteomics, and machine learning (ML) approaches to develop diagnostic classifiers, spatial tissue atlases, and identify potential therapeutic targets
-
Committee. You can read about the recruitment process at https://employment.ku.dk/faculty/recruitment-process/ . Interviews will be held on 27 April 2026.
-
: • Develop AI-driven control strategies for grid-forming inverters to enhance grid flexibility, reliability and stability. • Apply machine learning and AI tools for the battery system health estimation