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Field
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across all areas in Computer Science, including Algorithms and theory Bioinformatics and digital health Computing systems and networks Cybersecurity Human-computer interaction Machine learning and
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, in vitro models, and large collections of well-annotated clinical specimens. We employ state-of-the-art computational biology/bioinformatics approaches to dissect acute and adaptive responses to RAS
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externally (e.g., conferences) Required: PhD in Bioinformatics, Computer Science, Computational Biology, or related field Experience analyzing DNA sequencing data Proficiency in Python and/or R Familiarity
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central hub for Duke-NUS faculty specialising in Biostatistics, Bioinformatics, Systems Biology, Artificial Intelligence (AI), and other quantitative and data-driven sciences. CBDS functions as a strategic
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About You Essential Completion of a PhD in infectious disease epidemiology, veterinary science, animal science, immunology, bioinformatics, or a closely related discipline. Demonstrated postdoctoral
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) Mammalian cell culture (experience with induced pluripotent stem cells and/or adult stem cells preferred) Machine learning and basic bioinformatics A proven solid research experience in the context of multi
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Bioinformatics Core and Office of AI Research, with opportunities for all fellows to develop their own computational skills). Experimental validation – evaluating RNA drug candidates using cell-based assays
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involved in the bioinformatic and analytic aspects of predictive modeling. MINIMUM JOB QUALIFICATIONS: A Ph.D. in bioinformatics, genetics, statistics, mathematical, physical, or computer science
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epidemiology, bioinformatics, computational biology). Candidates who have successfully defended their thesis or dissertation are welcome, subject to evaluation on a case-by-case basis. Experience analyzing large
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discipline. You will have demonstratable experience in field-based research, microbiology (field and laboratory), bioinformatics (e.g. metagenomics and high-performance computing), and handling plants and soil