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Field
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Materials Generative Design and Validation Framework. The role will work at the intersection of machine learning, high-throughput experimentation, and materials discovery, focusing on accelerating the design
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or more of: the use of micro/nanofabrication and materials characterization tools; computational multi-physics/electromagnetics modelling and/or the application of machine learning algorithms; experimental
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, machine learning, mathematical modelling, or a related field, to join our research team in the Department of Applied Health Sciences. The successful candidate will work on an NIHR funded methodology project
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comprise both the development of bioinformatics pipelines and the application of novel machine learning methods for interpreting microbiome and host ‘omics data from faecal, intestinal biopsy and saliva
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, and may utilise iterative algorithms, machine learning and high-performance computing. Through the Monash Centre for Electron Microscopy, opportunities exist to acquire large experimental datasets using
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as world-leading or internationally excellent. We use this expertise to teach the next generation of health professionals and research scientists. Based across King’s Denmark Hill, Guy’s, St Thomas
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, leveraging advanced learning analytics, machine learning, and deep learning techniques. The candidate shall work under the supervision of the Principal Investigator (PI) and Co-PIs to conduct academic research
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qualification/experience in a related field of study. The successful applicant will have expertise in statistical modelling, epidemiology or machine learning and possess sufficient specialist knowledge in
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integration Key areas: § Metagenomic and 16S rRNA sequencing analysis § Single-cell RNA-seq and proteomic/metabolomic data integration § Machine learning and AI applications in microbiome data
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will conduct the lab experiment for RAS system for pollution control in recycled water in aquaculture system. He/she will also use machine learning tools to predict and optimize the RAS system. Job