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statistical software & machine learning (e.g., R, Python, SAS, or STATA). Experience working with large population dataset (e.g. EHR, claims data). Background in health informatics, population health research
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field assessments; experience in dataset organization, statistical analysis, and/or soil health assessment frameworks; previous publication(s) of scientific paper(s) on soil macrofauna; familiarity with
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, design, plan, and execute wet-lab experiments, conduct bioinformatic and statistical analyses, and write peer-reviewed journal articles. Key Responsibilities Design and implement experiments that pair
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statistics and standardize complex economic and ecological data into index-based scores for all Minnesota lakes greater than 10 acres. ● Coordinate with team members to integrate these ecosystem service values
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Max Planck Institute of Animal Behavior, Radolfzell / Konstanz | Konstanz, Baden W rttemberg | Germany | 5 days ago
analytical skills (Frequentist and Bayesian statistical frameworks) Experience in collecting and/or analyzing observational data on animal behavior, including ‘focal and party follow’ studies, as well as ‘ad
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of public health, biostatistics, pharmacoepidemiology and epidemiological studies. They must be proficient in data analysis using statistical or computer modelling software, such as Stata or R. They should
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. About the role As Postdoctoral Researcher in Big Data for Cardiovascular Population Health, you will play a key role in applying machine learning, health statistics, and large-scale medical and population
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, Agricultural Engineering, Environmental Engineering, Civil Engineering, Environmental Science, Hydrology, Data Science, Computer Science, Statistics, Geography, Water Resources, or a closely related field from
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computer science, data science, statistics, bioinformatics, electrical engineering, computer engineering, biomedical engineering, mechanical engineering, operations research, robotics, computational neuroscience
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, heart failure, long-term conditions, self-care or person-centred care. Survey data, registry or usage data, and statistical analysis. Multicentre research or collaboration with regions, clinical services