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Field
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), we aim to close this gap, by developing AI models and tools for tabular data, to help organizations, of any size, domain, and level of data literacy, get insights from structured data, efficiently
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. This position will focus on one key stream: understanding and improving the interaction between EVs and roadside barriers. This will involve: Modelling EV crash dynamics with conventional and SRB barriers
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R Experience with single-cell RNA-sequencing, in particular analysis of data would be an advantage Experience with mouse models and possession of a FELASA B certificate would be an advantage as both
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growing up in difficult conditions develop enhanced abilities, not despite, but precisely because of their challenging experiences. Research suggests that by navigating and negotiating diverse cultural
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combines microbial genomics, transcriptomics, and in vitro infection models to explore the adaptive trade-offs between drug resistance and virulence phenotypes. The position is funded for 3 years and
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: Develop innovative machine learning architectures for the mining, prediction, and design of enzymes. Combine state-of-the-art ML (e.g., deep learning, generative models) with computational biochemistry
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understanding on the origin of nucleic acids that are shed in liquid biopsies, such as blood, using cancer models (mouse and rat) and patient samples of neuroblastoma disease, a rare childhood cancer. Nucleic
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understanding on the origin of nucleic acids that are shed in liquid biopsies, such as blood, using cancer models (mouse and rat) and patient samples of neuroblastoma disease, a rare childhood cancer. Nucleic
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bottlenecks in data and system management, especially around data quality, metadata governance, and the integration of machine data for long-term monitoring. Through a hybrid approach combining physical models
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diverse academic backgrounds to contribute to our projects in areas such as: Network Security, Information Assurance, Model-driven Security, Cloud Computing, Cryptography, Satellite Systems, Vehicular