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metabolism Key Responsibilities Lead metagenomic, proteomics, and metabolomic profiling of clinical and experimental samples to identify dysbiosis signatures. Apply bioinformatics tools for microbiome data
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partners and microbiome researchers to integrate multi-omics data for comprehensive resistome analysis. Publish high-quality research articles and present findings at international conferences. Contribute
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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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foundation in Bio/chemistry, Bioprocess, and Microbial Biotechnology. Additionally, candidates should possess expertise in molecular biology and chemical analysis. Proficiency in biostatistical analysis, data
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: Present a table listing your indexed publications, arranged in descending order from the most recent to the oldest. Include information about your authorship position (e.g., First position) and provide
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sciences, or a closely related field. Proven experience in omics data generation and analysis (transcriptomics, proteomics, metabolomics). Strong background in microbial physiology, protein/peptide
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experience in natural product extraction, purification, and metabolite identification. Proficiency in the use of LC-MS/MS, GC-MS/MS, NMR, and relevant data analysis software (e.g., MestReNova, Thermo
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environmentally friendly alternatives. Funding information: This position is available for one year, extended to two years depending on the performance. Interested applicants should submit their online application
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include: A cover letter detailing the candidate's motivation for applying to this position. A comprehensive curriculum vitae. Brief research statement. Contact information for three referees. The candidate