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J., et.al . Predicting tumor cell line response to drug pairs with deep learning. BMC Bioinformatics. 2018; 19(Suppl 18): 486. doi: 10.1186/s12859-018-2509-3 Chiu Y., Chen H., Zhang T., Zhang S
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equivalent experience experience and proficiency in basic laboratory and/or bioinformatic techniques, including but not limited to animal handling, molecular biology, cell culture, and/or statistical analysis
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Computer Science, Computational Chemistry, bioinformatics) and have extensive experience in academia or industry with a focus in computational drug discovery. Prior experience working with GPCRs would be
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internally across the University community. Willingness to supervise PhD, Masters and/or Honours students. Desirable Knowledge and skills in bioinformatics for analysing metagenomes and microbial genomes
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research by integrating cutting-edge computational mechanistic modelling, bioinformatics, and artificial intelligence with experimental biology. Our multidisciplinary team operates across scales from
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of each research priority. Bioinformatics, artificial intelligence and computational biology Structural biology and small molecule drug development Pathogens of pandemic potential: Virology and
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publications and presentations. Demonstrate expertise in soil microbiomes, metagenomics, and plant-soil microbiome interactions. Have experience with soil incubations, bioinformatics, and statistical modelling
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of bioinformatics and bioscience data infrastructures at a national scale Actively support life science research communities with community scale digital infrastructure developed and maintained in concert with
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publications and presentations. Demonstrate expertise in soil microbiomes, metagenomics, and plant-soil microbiome interactions. Have experience with soil incubations, bioinformatics, and statistical modelling
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, and business, and is at the forefront of computing research in Australia and internationally with close links to major computing research initiatives, including Melbourne Bioinformatics, CSL