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Qualifications Doctoral degree in Bioinformatics or in biology with extensive experience analyzing omics data. Strong background in programming, algorithms and statistics. Familiarity with programming techniques
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! In this exciting role, you will leverage Artificial Intelligence,data science ,mechanistic models ,robotics , andsynthetic biology to enablequantitative predictions of biological systems and support
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scalable software and algorithms for genomic inference Collaborate with researchers across statistics, genetics, and computational biology Contribute to manuscripts, presentations, and open-source software
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lead the design, statistical optimisation and validation of assays for clinically relevant bladder cancer targets. Their central objective will be to develop an algorithmic workflow to detect new target
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biology, pathogen genomics, and computational approaches. This integration drives comprehensive studies of host–pathogen interactions across molecular, individual, and population scales to address emerging
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by applying existing and novel computational biology, bioinformatic, and machine learning algorithms to sequencing datasets and correlating them with multi-dimensional clinical datasets that contain
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this exciting role, you will leverage Artificial Intelligence, data science, mechanistic models, robotics, and synthetic biology to enable quantitative predictions of biological systems and support the
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biology with extensive experience analyzing omics data. Strong background in programming, algorithms and statistics. Familiarity with programming techniques for analyzing data sets and experience working
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The Duke-NUS Centre for Biomedical Data Science (CBDS) serves as a central hub for Duke-NUS faculty specialising in Biostatistics, Bioinformatics, Systems Biology, Artificial Intelligence (AI), and
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will focus on developing and applying computational and artificial intelligence approaches. Key Responsibilities: Conduct Scientific Research of Multi-omics Data Develop New Algorithms for Multi-omics