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—but are challenging to analyze. The project aims to move beyond black-box prediction by learning low-dimensional latent representations that capture these underlying physical processes. Examples
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. The project focuses on developing novel representation learning and generative modeling methods to construct a unified cellular morphology state space across heterogeneous datasets. By leveraging shared
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” and will be with Georg Oberdieck as a mentor. The main duty of the position is research within algebraic geometry or a closely related field such as geometric representation theory. The following
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transferable and interpretable models for tabular data, efficient learning paradigms for medical imaging, and causally grounded and identifiable representation learning. You will have great freedom to influence
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scattering and absorption and black carbon. Several years of aerosol-dynamics and Lagrangian box-model simulations including runs with modified land use and aerosol process representations will be analyzed