Sort by
Refine Your Search
-
the role The PDRA will carry out research on probabilistic circuits (PCs) and tree-based machine learning methods for generative modelling, with a particular focus on responsible AI. The project builds
-
to work on the discovery of new superconducting materials with high critical temperatures, using novel methods and concepts such as machine learning and quantum geometry. The project is related to large
-
, machine learning, multiscale and multiphysics simulation, computational anatomy, medical image analysis, and integration of wearables and biosignal processing, applied to conditions ranging from cardiac
-
and Sobolev-type spaces (with Hytönen and/or Korte), Conformal deformations of metric measure spaces and/or general regularity and convergence for graph-based machine learning using stochastic game
-
the role The PDRA will carry out research on probabilistic circuits (PCs) and tree-based machine learning methods for generative modelling, with a particular focus on responsible AI. The project builds
-
| Collective bargaining agreement: §48 VwGr. B1 lit. b (postdoc) Limited until: 31.03.2032 Reference no.: 5115 Explore and teach at the University of Vienna, where over 7,500 brilliant minds have found a unique
-
postdoc may develop and teach courses aligned with their expertise, in consultation with the PI and curriculum coordinators. Participate in evaluation measures and quality assurance This is part of your
-
(postdoc) Limited until: permanent Reference no.: 4984 Among the many reasons to research and teach at the University of Vienna there is one in particular, which has convinced around 7,500 academic staff
-
to develop novel computational methods for data integration and analysis Experience with machine learning approaches for biological data modeling and predictive analytics Good communication skills and
-
metabolism Strong problem-solving skills and the ability to develop novel computational methods for data integration and analysis Experience with machine learning approaches for biological data modeling and