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Field
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The doctoral student project and the duties of the doctoral student This Data Driven Life Sciences (DDLS) PhD project focuses on probabilistic models of protein structure, which can be used primarily
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powerful framework for decentralised machine learning. FL enables multiple entities to collaboratively train a global machine learning model without sharing their private data, thus enhancing privacy
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and global ecosystems. The SciLifeLab and Wallenberg National Program for Data-Driven Life Science (DDLS) aims to recruit and train the next generation of data-driven life scientists and to create
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and robustness of defenses and mitigations for AI systems, reverse engineering AI systems and models, and identifying new areas where security research is needed. We participate in communities
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, model data integration, data mining, land surface models, ecosystem fluxes, isotope methods, biodiversity, organismic interactions, biological mineral formation, palaeoclimatology, micropalaeontology and
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to trick AI-based models, pay little attention to fake-normal data traffic generated by Generative Adversarial Networks (GAN). This PhD research will address a major vulnerability in AI based smart grids by
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testing and computational modelling. You'll become part of a diverse, multidisciplinary team that prioritises equity, diversity, and inclusion, gaining specialist expertise in hydrogen-material interactions
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-class facilities, enhancing their skills in materials characterisation, computational modelling, and experimental testing. These experiences will position the graduate as an innovator, ready to
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-engine aerodynamics. The project is aligned with the acknowledged skills development needs in the areas of aircraft/propulsion integration, aerodynamics, modelling, software development, research testing
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and robustness of defenses and mitigations for AI systems, reverse engineering AI systems and models, and identifying new areas where security research is needed. We participate in communities