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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | about 24 hours ago
requested or required to support the work of the research project. Minimum Education and Experience Requirements PhD in one of these fields: Computer Science, Data Science, Biomedical Informatics
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intervention trials (e.g., diet, FMT), and ex vivo gut models enabling advanced multi-omics analyses of these samples. In addition the lab also maintains a large culture collection, partially linked to genomic
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. The candidate must hold a PhD in Urban or Rural Development, Civil Engineering or related domain. The candidate is expected to have hands-on experience in field related to urban or rural planning, renewable
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development. Basic Qualifications: A PhD in computer science/engineering, electrical engineering, data science or a related field completed within the last five years. Experience of AI and efficient computing
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trials (e.g., diet, FMT), and ex vivo gut models enabling advanced multi-omics analyses of these samples. In addition the lab also maintains a large culture collection, partially linked to genomic data
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scholars in Law and two PhD students (one in Law and one in Computer Science/Data Analytics), as well as with international, European and national stakeholders involved in the CURE project. The post-holder
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multivariate data analysis. Preferred Qualifications: PhD in Chemistry, Materials Science or Chemical Engineering. Candidates are expected to have a strong background in quantitative structure analysis based
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-actuated large-scale manipulators (e.g., container cranes). For further information on Dr Antoine Cully’s research and projects, see www.imperial.ac.uk/adaptive-intelligent-robotics This project will be
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productivity and scientific development by exploiting our group's large suite of crystal growth and characterization tools, as well as extensive collaboration and local scientific and technical expertise. Major
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high fidelity models of ice crystal icing accretion and shedding, verifying tools using the wealth of unique experimental validation data generated by researchers at the Oxford Thermofluids Institute