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issues in federated and decentralized learning systems. The aim is to develop novel methods for securing communication against passive and active adversaries, leveraging tools from statistical estimation
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: · A vibrant and collaborative research environment, supported by experienced supervisors with expertise in wildlife ecology, conservation technology, applied statistics, and deep learning · Access
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theoretic understanding of the fundamentals of Machine Learning. Ability to act independently as well as to collaborate effectively with members of a larger interdisciplinary team, take initiative, be result
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healthcare. They collaborate with partners from all over the world to improve health outcomes for all. Radboudumc unites patient care, research, education, and corporate learning, which allows us to approach
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pride in being an inclusive community of outstanding learners, investigators, clinicians, and staff where interdisciplinary collaboration is embraced and great ideas accelerate translation of fundamental
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maintain state-of-the-art preprocessing and analysis pipelines. Write manuscripts for publication in leading international journals, in collaboration with the PI and the research team. Present scientific
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Understanding plankton biodiversity and ecosystem change by applying machine learning – A CASE studentship Lead Supervisor (DoS): Professor Abigail McQuatters-Gollop Second Supervisor: Dr Clare
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technology, practice, and learning, with a particular focus on how Work-Integrated Learning can contribute to developing professional competence in digital crime prevention. The PhD student will collaborate
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using cutting-edge computational techniques, including machine learning algorithms. Work collaboratively with an interdisciplinary and international team to refine and validate regional wave and ocean
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Applying machine learning-guided directed evolution to improve multiple enzyme properties Upscaling selected biotransformation reactions in collaboration with academic partners Responsibilities and