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science, computer science, applied mathematics or a related field a strong background in machine learning, material modeling, and metals processing, modeling and simulation (This will be a clear advantage
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will be trained in a variety of powerful, modern analytical techniques including chemical proteomics and metabolomics. They will have access to advanced synthesis facilities, as well as biological models
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powerful, modern analytical techniques and have access to advanced synthesis facilities, as well as biological models, such as macrophages and organoids, to enable the rapid translation of this research
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? Generative AI and large-language models (LLMs) are about to turn computer-aided engineering into true human–AI co-design. In the new MSCA Doctoral Network GenAIDE we team up with Honda Research Institute
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) Project Why apply? Generative AI and large-language models (LLMs) are about to turn computer-aided engineering into true human–AI co-design. In the new MSCA Doctoral Network GenAIDE we team up with Honda
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Why apply? Generative AI and large-language models (LLMs) are about to turn computer-aided engineering into true human–AI co-design. In the new MSCA Doctoral Network GenAIDE we team up with Honda
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well as their cleanroom fabrication by silicon micromachining will be investigated. The main challenges are (1) the design and modelling of new sensor topologies, (2) development of the MEMS fabrication processes, (3
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, transcriptomics, and proteomics and work on advanced biological systems for modelling infection including macrophages, organoids, zebrafish, primary and clinical samples. Embedding within the Institutes of Biology
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mathematical microbial kinetics models and quantitative risk assessments; develop user-friendly tools for quality and safety assessment of plant-based meat analogues; supervise BSc and MSc thesis research
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of methane dynamics in rapidly changing ecosystems and contribute to improving predictive models of future methane emissions. Field sampling will focus on regions where methane cycling is still poorly