-
vibrant, internationally oriented research environment with a strong balance between theory and real-world applications. The division conducts high-quality research, doctoral education, and undergraduate
-
-temporal machine learning method development, including: generative models for grid-based and particle-based spatio-temporal data; controlled generation methods for data assimilation; and graph-based multi
-
, numerical analysis, approximation theory, or equivalent subjects. Alternatively, you have gained essentially corresponding knowledge in another way. Candidates should have experience in the following
-
engineering (focusing on deep learning for computer vision), and the division of statistics and machine learning at the department of computer and information science (focusing on the theory behind machine