-
knowledge gained from climate models to applied researchers and decision makers are encouraged to apply. A Ph.D. in physical geography, statistics/biostatistics, environmental, climate, atmospheric, physical
-
in R for conducting multivariate statistics and deep learning analyses using discrete and continuous phenotypic data; Assist lab members on conducting Python-based Bayesian inference analysis
-
time scales. The core responsibilities of the Research Specialist will include: expand/develop code in R for conducting multivariate statistics and deep learning analyses using discrete and continuous
-
to centennial changes in the statistics of tropical cyclones over the past millennium, recent historical era and the coming centuries, using a combination of high-resolution climate modeling, machine learning
-
the Study Abroad Fair, Passport Day, and other SAP program events; assists with providing reports, statistics, and analyzing study abroad data as needed (e.g., for the nation-wide Open Doors study abroad
-
of tools and public goods. Requirements Applicants must have (or expect to have) a PhD in the social sciences, statistics, computer science, or related fields, and their interests must fall at the technical
-
troubleshooting experimental protocols 3. Processing, organization and storage of sequencing data - Run basic sequence alignment and quality filtering protocols - SNP calling and summary statistics of large
-
information. Responsibilities will include establishing and managing collaborative, multisite research projects; conducting advanced statistical analysis on complex data sets; disseminating research through
-
letters) for at least 2 references (we will contact references for letters at a later stage). PREFERRED - Experience working with REDCap - Basic statistics training (e.g., ANOVA, t-tests, use of SPSS and R
-
Ph.D. in quantitative ecology, climate sciences, population biology, applied mathematics, statistics, or an equivalent discipline. Additional experience in climate, meteorological, or environmental data