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Skip to main content. Profile Sign Out View More Jobs Research Assistant: register data from Statistics Denmark - DTU Management Kgs. Lyngby, Denmark Job Description The Division for Climate and
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Skip to main content. Profile Sign Out View More Jobs Research Assistant in statistical analysis of hybrid work data sets - DTU Engineering Technology Ballerup, Denmark Job Description The Research
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Postdoc in statistics to develop Bayesian privacy metrics for synthetic health data (2024-224-05725)
on Bayesian statistics and apply them in several real-world settings of important clinical relevance. The postdoc will be responsible for developing the area with a group consisting of a PhD student, a data
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University, a leading international research group investigating psychiatric epidemiology and statistical genetics. The Centre has a strong track record in collaboration with other Danish researchers and with
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mitigation strategies. Tasks and responsibilities: Using statistical signal processing methodology to develop methods of fault detection for snifferes measurement system. Develop methods of noise filtering
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parameters. The model is used for condition monitoring and fault detection using methods focusing on statistical methods using residual generation and Kalman filtering. Qualifications: Phd and master's degree
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PhD fellowship in conservation science - developing and validating indicators of ecosystem integrity
experience in field, database and GIS work on biodiversity and ecosystem processes, such as ecohydrology, nutrient dynamics and carbon pools, as well as statistical modelling will be considered assets.Fluency
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to the continuous development of the Gaussian-Linear Hidden Markov Model ( GLHMM ) toolbox. In this role, you will be responsible for applying and validating statistical testing methodologies using datasets obtained
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methods and techniques to study and characterize sound and vibration on the millimetre and sub-millimetre scale as well as statistical methods to evaluate metrological aspects such as reproducibility
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on developing statistical and quantitative modeling approaches to genomic and phenomic data and apply these models for understanding the genetic mechanisms underlying variation in agronomic traits