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models or inflammatory bowel disease (IBD). Familiarity with metabolomics (LC-MS, GC-MS) and analysis of microbial metabolites. Expertise in interkingdom microbiome research (fungi, viruses, archaea
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. The successful candidate will answer questions such as how to assign limited communication resources to train the federated machine learning model efficiently. She/he will investigate realistic scenarios including
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for microbiome analysis. Publication record in peer-reviewed journals. Preferred Qualifications Experience with colorectal cancer models or inflammatory bowel disease (IBD). Familiarity with metabolomics (LC-MS
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an innovative approach, UM6P places research and innovation at the heart of its educational project as a driving force of a business model. In its research approach, the UM6P promotes transdisciplinary
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to environmental temperature stress during early development stages. This research will involve high-throughput omics, physiological assays, and computational modeling to design and validate stress-priming protocols
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communication resources to train the federated machine learning model efficiently. She/he will investigate realistic scenarios including non-iidness of data distribution, system heterogeneity, and dynamic
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an innovative approach, UM6P places research and innovation at the heart of its educational project as a driving force of a business model. In its research approach, the UM6P promotes transdisciplinary