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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 Be the First to Apply Job Description The
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Professorship in Statistics at the Center for Statistics, Department of Finance. The Department of Finance carries out research and teaching in a wide range of financial topics but also includes the Center
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Would you like to work with statistical analysis? Then you might be what we are looking for. We are looking for a student assistant to help and assist with Multi-level analysis and SEM. Your tasks
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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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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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Natural Sciences Tenure Track Aarhus University offers talented scientists from around the world attractive career perspectives via the Natural Sciences Tenure Track Programme. Highly qualified candidates are appointed as Assistant Professors for a period of six years with the prospect of...
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statistical modeling and methods development to experimental hypothesis evaluation and clinical translation. There are also close collaborations with the wet-lab and clinical groups at the department ( https
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languages (eg, R, Python), implement statistical associative models (eg, GLMM), as well as experienced in simulation development (eg, multi-agent based models). You will also work with stakeholder engagement
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of basic statistical, quantitative analysis techniques are desired. Applicants are expected to have an understanding of certain statistical software (e.g. SPSS, SAS). Experience with data modelling is a
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.) for optimal operation using e.g. model predictive control. You will use stochastic and statistical modelling concepts together with domain-knowledge to develop such models. Afterwards, the models are used in