Sort by
Refine Your Search
-
Listed
-
Employer
- Indiana University
- The University of South Dakota
- University of Maine
- University of Washington
- Argonne
- Brookhaven Lab
- Dartmouth College
- Duke University
- Emory University
- Fred Hutchinson Cancer Center
- Georgetown University
- Johns Hopkins University
- Lawrence Berkeley National Laboratory
- National Aeronautics and Space Administration (NASA)
- North Carolina State University
- Oak Ridge National Laboratory
- Purdue University
- The University of Arizona
- University of California Davis
- University of Florida
- University of Idaho
- University of Minnesota
- 12 more »
- « less
-
Field
-
contribute to the excellence of our academic community. We are looking for a postdoctoral researcher with expertise in Bayesian hierarchical spatio-temporal statistics and measurement error methods for a 3
-
quantitative and analytic skills. Preferred Qualifications Experience with evidence-accumulation models (DDM, sequential sampling, Bayesian models). Experience with computer vision tools (e.g., MediaPipe
-
spatial and temporal scales, leveraging cutting-edge hierarchical Bayesian modeling approaches. The Fredston Lab uses large datasets, theoretical models, and a range of statistical tools to predict marine
-
candidates with strong expertise in Bayesian methods, uncertainty quantification, and/or machine learning applied to nuclear theory. The group’s research spans a wide range of topics including nuclear
-
-ray Magnetic Circular Dichroism (XMCD), X-ray imaging or resonant magnetic scattering. Demonstrated ML experience (e.g., dimensionality reduction, spectral unmixing, Bayesian inference, or physics
-
, macroinvertebrate collection, and stable isotope analysis. The successful candidate is expected to have extensive experience in aquatic ecology, coding in R, and an ability or willingness to learn Bayesian modeling
-
, macroinvertebrate collection, and stable isotope analysis. The successful candidate is expected to have extensive experience in aquatic ecology, coding in R, and an ability or willingness to learn Bayesian modeling
-
, macroinvertebrate collection, and stable isotope analysis. The successful candidate is expected to have extensive experience in aquatic ecology, coding in R, and an ability or willingness to learn Bayesian modeling
-
experience in one or more of: large-scale data analysis, time-series photometry, spectroscopy, astrometry, Bayesian/statistical inference, and/or software development for astronomical datasets. Department
-
career scientist with background in organic geochemistry, statistics, and Bayesian modeling to pursue analyses of paleoclimate biomarker data. The ideal candidate should be proficient with both laboratory