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strategies such as molecular glues and targeted protein degradation. Our research integrates molecular and cell biology, biochemistry, genetics, and computational approaches to identify and exploit cancer
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Spectrometry This doctoral project aims to improve our understanding of chemical contaminants in food, focusing on detecting both known and previously unidentified compounds using suspect and non-target
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learning or deep learning, preferably with transformer architectures Experience in probabilistic modelling or Bayesian statistics Programming skills in Python, preferably with PyTorch or similar frameworks
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and propagate uncertainty from image features to predicted PEMWE behavior. Bayesian experimental design and process optimization. The digital twin will form the basis for Bayesian optimization
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: Bayesian hierarchical deconvolution of spatial bins using matched snRNA-seq reference, cell-cell communication inference, and spatial niche identification Multi-omics integration: linking spatial and single
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conduct research in several areas: analysis of high-dimensional data, Bayesian methods, spatial-temporal models, non-Gaussian modeling, applied research in social science, as well as stochastic models and
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. They have led to a plethora of important downstream applications, such as image and material generation, scientific computing, and Bayesian inverse problems. At the core of these models are differential
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staff. We conduct research in several areas: analysis of high-dimensional data, Bayesian methods, spatial-temporal models, non-Gaussian modeling, applied research in social science, as well as stochastic
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conduct research in several areas: analysis of high-dimensional data, Bayesian methods, spatial-temporal models, non-Gaussian modeling, applied research in social science, as well as stochastic models and
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dynamics and behaviour, e.g. aerial/drone surveys, line transects, camera surveillance and photo-ID. Experience with Bayesian statistical modelling Proven ability to handle large ecological datasets and