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national regulations. Please find additional information in the Information package for Marie Curie fellows in doctoral networks (https://op.europa.eu/s/z831 ). Selection process For the selection procedure
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, the purpose for which your data will be processed, as well as further information about data protection is available to you on the website: https://tu-dresden.de/karriere/datenschutzhinweis .
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» Applied physics Technology » Medical technology Engineering » Biomedical engineering Computer science » Modelling tools Economics » Health economics Biological sciences » Other Researcher Profile First
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computer science, applied mathematics, physics, engineering, biology, plant and agrosphere sciences, or another relevant natural science; Strong interest in interdisciplinary research at the interface of natural
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of the Future. https://cordis.europa.eu/project/id/101226431 This network has 8 host institutions hiring doctoral candidates: Uppsala University, Universitat Politecnica de Catalunya, Dublin City University
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(https://soilsystems.net/ ), a Priority Programme (SPP 2322) funded by the Deutsche Forschungsgemeinschaft (DFG; German Research Foundation). Within SoilSystems, scientists from different disciplines from
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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