Sort by
Refine Your Search
-
Listed
-
Employer
-
Field
-
society. Central themes of teaching and research include Digitalisation, Machine Learning, Autonomous and Intelligent Systems, Cybersecurity and Health Technology. The Faculty of Technology is the newest
-
Structures (FiRST). The starting date is March 1, 2026, or as mutually agreed. The positions are for a period of two years, with a possible one-year extension. Your experience We expect you to have a PhD
-
of Computing The Department of Computing educates professionals for the modern digitalised society. Central themes of teaching and research include Digitalisation, Machine Learning, Autonomous and Intelligent
-
to bridge preclinical findings with clinical applications. Through advanced computational approaches, machine learning, and AI-driven neuroinformatics, we extract meaningful patterns from omics, imaging
-
(linking phenotypes, imaging, cytometry, or other readouts to transcriptomics) Statistics / machine learning for biological inference (model validation, differential state testing, embeddings/classifiers
-
-effectively predicting the rate of massively multicomponent organic, or organic-enhanced, new-particle formation in the atmosphere. We will combine our molecular-level model development with machine learning
-
organic, or organic-enhanced, new-particle formation in the atmosphere. We will combine our molecular-level model development with machine learning and artificial intelligence methods, targeted validation
-
have solid skills in programming and working with libraries for training and using machine learning models. Previous experience in managing large volumes of data and high-performance computing is
-
net period of time, which does not include parental leaves, military service etc.) good skills in spoken and written English motivation for research work in aerosol physics or chemistry. Please note
-
volumes of audiovisual data is essential. The appointee must have solid skills in programming and working with libraries for training and using machine learning models. Previous experience in managing large