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repeat the process of domestication (i.e. neo-domestication). Task The postdoctoral fellow will work on a three-year project aimed at the investigation and utilization of natural variation occurring in
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the candidate’s qualifications and experience; benefits include medical and dental insurance, housing on the KAUST campus, annual travel allowance and paid vacation, and other generous benefits. Saudi Arabia is a
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, air-conditioned, campus-based accommodation with house maintenance Educational allowance for school-age children Free medical and dental care at the University Clinic or local hospital Excellent
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King Abdullah University of Science and Technology (KAUST) | Saudi Arabia, | Saudi Arabia | 2 months ago
) for analysis and prediction based on medical or biological imaging data Bioinformatics: methodology for the analysis, integration, and prediction based on high-throughput molecular data Biomolecular design
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) for analysis and prediction based on medical or biological imaging data Bioinformatics: methodology for the analysis, integration, and prediction based on high-throughput molecular data Biomolecular design
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) for analysis and prediction based on medical or biological imaging data Bioinformatics: methodology for the analysis, integration, and prediction based on high-throughput molecular data Biomolecular design
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, particularly in Catalysis, Membranes, and Solar Energy. About the PSE Division and KAUST The ChemS program belongs to the Physical Science and Engineering Division that comprises seven Degree Programs: Material
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, deep learning, and AI) for analysis and prediction of genotypic variation Methodology (machine learning, deep learning, and AI) for analysis and prediction based on medical or biological imaging data
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engineering for alternative carbon source utilization, and process design for biosystem production Bioprocess engineering and pharmaceutical grade cell expression processes (Car-T cells): optimizing and scaling
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-inspired approaches for modeling, designing, and predicting the response of composite systems. Responsibilities: Develop AI approaches for predictive multi-physics response of composites in Energy