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This PhD project focuses on strengthening network security for large-scale distributed AI training. As training increasingly spans multiple data centers connected over wide-area networks, it
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within a BSc/MSc thesis project. · Ability to effectively and reliably coordinate with the supervisor and multiple collaborators Language skills · Good level both written and spoken
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approaches, including Low Impact Development (LID) practices (e.g., green roofs, rain gardens), with a specific focus on urban catchments. The research will place a strong emphasis on machine learning
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Generative machine learning models have made significant progress in recent years. Typical examples include, for example, high-quality image or video generation using diffusion models (e.g
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colleagues at Public Health to evaluate the development of a Mental Wellbeing approach that is designed with and delivered by the Voluntary, Community, Faith and Social Enterprise Sector. The learning from
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one of the following analysis techniques (multiple preferred): normative modelling, dimensionality reduction techniques, machine learning, deep-learning, state space modelling, advanced statistics
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to learn unfamiliar tools as needed (e.g. GitHub, APIs, new libraries). Familiarity with econometric techniques, especially panel data methods. Clear, concise written and verbal communication skills. Ability
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seeking a Research Assistant Professor that will be capable of contributing to multiple ongoing research projects in the lab. Potential projects include, but are not limited to, oceanographic
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similar field; expertise in programming skills and statistical data analyses, including machine learning; affinity with environmental exposure modelling and high-performance computing; strong reporting and
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interest in environmental health and Exposome research; expertise in programming and quantitative data analysis, including machine learning in R/Python; affinity with bioinformatics; strong collaboration