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to study translational aspects of cancer (single-cell sequencing of immune cells, organoid co-cultures, cellular engineering via CRISPR/Cas9 technology, in vivo imaging, advanced animal models of allo-SCT
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working in interdisciplinary and international teams and have image processing or image analysis skills. In addition, you are able to express yourself confidently both orally and in writing in English. What
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for benchmarking existing state-of-the-art (SOTA) Federated Learning algorithms. This includes running a few pre-processing pipelines. Develop SOTA FL algorithms that tackle data heterogeneity; namely non-iid and
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physics, biomedical/material engineering or a related discipline. You have a strong background in data analysis and image processing. You enjoy working in interdisciplinary and international teams and have
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algorithms. This includes running a few pre-processing pipelines. Develop SOTA algorithms in Robustness against data and model poisoning attacks. Develop SOTA algorithms in the Explainability of Deep Federated