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learning Distributed and federated training The candidate is expected to hold a relevant MSc degree in Computer Science, Data Science, Physics, (Applied) Mathematics, Computational Statistics or another
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Denmark. The work consists of quantitative research, including developing research questions, conducting theory-driven statistical analyses of longitudinal register data, and, where relevant, linking
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, data sciences, applied mathematics, statistics, operations research, control, electrical engineering, energy systems, cyber security, or related fields Knowledge of privacy-preserving mechanisms
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of skills, and have the opportunity to explore interests in polymer synthesis, assay development, immunology, and statistics. Specifically, the work will involve: Organic synthesis Assaying polymers
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of machine learning Distributed and federated training The candidate is expected to hold a relevant MSc degree in Computer Science, Data Science, Physics, (Applied) Mathematics, Computational Statistics
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this study) and the novel models and data established in this project. Application of statistical methods to deal with the uncertainty and the probabilistic nature of the models. This study requires strong pre
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, including design, materials and processes, or testing, would strengthen your application. Finally, a background or keen interest in statistics, statistical analysis, and surrogate-based modelling would be
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computational chemistry or physics will be preferred, but candidates with a solid background in statistics, computer science, and/or mathematics are also encouraged to apply. Programming skills (e.g., Python
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to be fluent in English (written and spoken), hold a degree within epidemiology, statistics or a health-related discipline and have a track-record within the field of observational research, preferably
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biology, pharmacology and/or immunology. Experience in statistical bioinformatics, including developing analysis pipelines and applying programming (R/Python) to genomic/transcriptomic data. Strong written