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of the designed algorithms and systems. Help with research presentation works such as high-quality paper writing. Job Requirements: Preferably PhD in Computer Engineering, Computer Science, Electronics Engineering
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, early life antibiotic use, ultra-processed foods, and microbiome changes. The research team also develops machine learning models to predict cancer risk from longitudinal medical data. For more details
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regional participants technical laboratory (wet lab) and bioinformatics (dry lab) training in pathogen genomics. The Emerging Infectious Diseases (“EID”) is a Signature Research Programme of Duke-NUS
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microbiome or bacterial-host bioinformatics is required. Proficiency in raw data processing, taxonomical annotation, and analyses of 16s rRNA datasets (ie DADA2, MaSalin2, Random forest analyses etc
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bioinformatics experts to assess library quality and performance. Contribute to regular project meetings, reporting progress, troubleshooting technical challenges, and proposing experimental improvements. Support
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to the programme of research. Job Requirements: PhD’s degree in Computer Engineering, Computer Science, Electronics Engineering or equivalent. Independent, highly analytical, proactive and a team player Excellent
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. Bachelor of Science in Chemistry Advanced knowledge of bioinformatics tools and packages, including experience in processing and analysing high-throughput sequencing data. Highly proficient in R, Python, and
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digital signal processing, control systems, and embedded AI for biological applications. Experience with AI/ML development frameworks, model deployment pipelines, and simulation environments for behavior
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of this program is to discover clinically relevant candidates that can be fast tracked for clinical development. The lab uses a combination of bioinformatics, biochemistry, animal modelling, Crispr screening and
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, metabolomic and metagenomics data Large-scale clinical and molecular phenotypes data, including integrative omics studies Evaluation and application of appropriate bioinformatics/statistical techniques, as