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learning and Bayesian statistics. FLSA Exempt Grade 06 Salary Details $82,166 - $90,382 Minimum Salary 82166.000 Mid Range Salary 104002.000 Maximum Salary 125837.000 Offer Information The final salary offer
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to offer. Qualifications: Required: PhD in ecology by start date Experience in plant phenology, biogeography, and spatial and temporal modeling (Bayesian and frequentist) Expertise in R or Python, GIS, big
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spans from advanced theoretical and methodological Statistics (classical and Bayesian) to diverse applications, allowing for comprehensive research approaches. Our members work on Design of Experiments
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experiments. The objective is to develop Bayesian causal models and neural networks capable of identifying relevant causal relationships between instrumental parameters and observed anomalies. The work will
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Dalhousie University | Halifax Mid Harbour Nova Scotia Provincial Government, Nova Scotia | Canada | about 15 hours ago
Python programming. Experience guiding trainees in bioinformatics skills. Advanced knowledge of phylogenomic analyses and the use of site-profile mixture models in a Bayesian and Maximum likelihood context
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spans from advanced theoretical and methodological Statistics (classical and Bayesian) to diverse applications, allowing for comprehensive research approaches. Our members work on Design of Experiments
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interpretations, in this project, we will use Bayesian statistics and phylogenetic methods to evaluate whether the timing of CSP clade evolution and HGT events are consistent with the oxygenation timeline
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of using Bayesian methods in both model development and fitting. Previous experience and knowledge of research methods and study design in clinical trials. Knowledge of Good Clinical Practice (GCP) in
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function differentiation, compositional Bayesian inference techniques); analyzing what is required (e.g., choice of data structures, static analyses and compiler optimizations, parallelism and concurrency
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/Seurat, count models, batch correction, differential analyses). Strong grounding in statistics (GLMs, hierarchical/Bayesian modeling, multiple testing) and experimental-design principles. Bioinformatics