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RNA-seq data for testing and validation. Perform data analysis on large-scale RNA-seq data in pediatric cancer. This may involve the analysis of both scRNA-seq, bulk RNA-seq as well as new single-cell
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Framework (RDF). enables advanced data mining queries using the SPARQL query language. provides a natural language-based interface to perform these queries on the knowledge graph using a large language model
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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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of mouse behavioral paradigms is required. Extensive computer programming experience in python or Matlab and the ability to deal with large, complex datasets are required. Experience with preparation and
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Continuum mechanics Large deformation elasticity Design of mechanical devices Programming skills As a formal qualification, you must hold a PhD degree (or equivalent). We offer DTU is a leading technical
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amenable to therapeutic targeting. This position will involve the application of advanced data science approaches to explore large-scale clinical datasets extracted from electronic health records, with
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and reduction of very large data sets, data analysis, and simulations of X-ray scattering and spectroscopy signatures of dynamic processes in battery materials. The theoretical/ simulation efforts
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, creative start-ups, big data, big ambitions, hands-on learning, and a whole lot of robots, CMU doesn’t imagine the future, we invent it. If you’re passionate about joining a community that challenges the
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with the Principal Investigator Professor Cormac Sreenan and the gVID project Technical Lead. A suitable candidate will have a PhD, 8 years of research/industrial experience and a keen interest in
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: Masters and subsequent PhD in Physics, Chemistry, Material Science or related disciplines Experience in neutron spectroscopy, e.g. INS, QENS, NSE Good knowledge of the structural characterization of matter