-
About the lab The Laboratory of Causal Systems Immunology combines causal inference, probabilistic AI and large-scale in vivo perturbation experiments to uncover how genes shape immune-cell states
-
, Apply foundation models to analyze medical imaging data. Develop probabilistic deep learning models to capture temporal patterns in chronic disease progression and treatment response. Ensure model
-
to 2 postdoctoral researchers on technology foresight for policy-driven low-carbon technologies. The postdoctoral position will focus on developing data-driven approaches for probabilistic modelling
-
The past decades have been associated with substantial losses of sea ice over both hemispheres. Existing climate models are currently unable to accurately forecast these changes, in part due
-
associated with substantial losses of sea ice over both hemispheres. Existing climate models are currently unable to accurately forecast these changes, in part due to their imperfect representation of ocean
-
Experience in one or more of the following areas: object detection and segmentation, multi-object tracking, time-series analysis, probabilistic modeling and uncertainty quantification, real-time or streaming
-
description Work on EU projects to develop next‑generation transport, emission and health forecasting models by integrating deep learning, xAI, and diverse data sources such as traffic sensors, smart‑card data
-
the coupled physical process of GCS, such that we can efficiently forecast the spatial-temporal patterns of the subsurface response variables, e.g., pressure, saturation, minerals etc.; (2) integrate