4 ensemble-prediction Postdoctoral positions at Technical University of Munich in Germany
-
planning and control algorithms Multi-modal perception techniques (e.g., vision, tactile, force) Machine learning models for physical behavior prediction and manipulation strategy adaptation Real-world
-
will train a physics-informed neural network (PINN) for fast, precise predictions of pressure, density, and velocity fields. The project also includes producing feed spacer prototypes through 3D printing
-
(songbird) variables, and human restoration variables. We aim to produce maps of model predictions based on biodiversity indices, restoration variables in relation to urban green and built infrastructure. Job
-
Transferability, as well as Deep Learning for Complex Structures. These novel methods will be applied to practical tasks such as predicting European water storage, quantifying permafrost thawing, sea level budget