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in large pre-trained models (vision-language models), generative models (flow matching, diffusion), simulation-based inference, and robust and active learning. The group has a wide network of
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to improve the biological understanding of lymphoma by characterizing tumor cells, immune cells, and their spatial organization within the tumor microenvironment. We use spatial transcriptomics and spatial
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Infrastructure? No Offer Description Postdoctoral studies single-cell and computational biology(scholarship) Do you want to contribute to better health for all? A postdoctoral position is available in
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. Simulation-based inference (SBI) addresses this by training neural networks, such as flow-matching generative models, on simulated events. The project aims to develop efficient, robust and calibrated SBI
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(NC). In the project, we aim to establish a reliable analysis package for nitrocellulose. Your role is to develop and validate methods for the key properties of NC – including degree of substitution
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and variation. The successful candidate will develop innovative methods and models to advance our understanding of genome evolution and variation. The position is based in the Computational Genomics
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and variation. The successful candidate will develop innovative methods and models to advance our understanding of genome evolution and variation. The position is based in the Computational Genomics
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for the circular utilisation of by-products from dairy production. The research includes membrane-based separation and concentration processes, including ultrafiltration, nanofiltration and electrodialysis, as
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-doctoral posistion is to conduct research. Teaching may also be included, but up to no more than 20% of working hours. The position shall include the opportunity for three weeks of training in higher
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cells, and their spatial organization within the tumor microenvironment. We use spatial transcriptomics and spatial proteomics, advanced image analysis, and computational approaches to investigate