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Field
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collaboration with other researchers. Your profile • Formal requirement: Doctoral degree (PhD) in quantitative genetics, tree breeding, biometrics, statistical genetics, bioinformatics or related field
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software modules, protocols, or frameworks that use NIST-standardized PQC algorithms Evaluate performance, interoperability, and security implications of replacing classical cryptographic schemes with
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software modules, protocols, or frameworks that use NIST-standardized PQC algorithms Evaluate performance, interoperability, and security implications of replacing classical cryptographic schemes with
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Overview This PhD project will take a comprehensive approach to exploring how corporate sustainability disclosure practices evolve in response to the integration of Machine Learning (ML) within
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uses machine learning to improve online accelerator models and that develops correction algorithms for accelerator operations. This position is for a 2-year research project. This position has a high
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responsible for teaching six courses over the academic year in introductory programming, algorithms, machine learning, or systems depending on the candidates’ qualifications and programmatic need. In
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pipelines. Assists in analysis pipeline and algorithms development and validation. Extracts feature of interest from data relating to specific biological questions. Contributes to the development and
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scientists at the Algorithmic Transparency Institute (a project of the National Conference on Citizenship) and other academic and non-profit organizations on data collection, analysis, academic reporting
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team. You will lead in the design and implementation of statistical and computational algorithms of different datasets, and implement novel algorithms within the framework of existing code, providing
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the best performance from the CCD during the SMILE mission lifetime. Support the analysis of the SMILE CCD radiation damage studies and support development of radiation correction algorithms for the SMILE