Sort by
Refine Your Search
-
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
-
, 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
-
) physics. Desirable Expertise in computational fluid mechanics, broadly construed. Expertise in Bayesian methodology for optimization and experiment design. Experience with equivariant neural networks. Track
-
-series photometry, spectroscopy, astrometry, Bayesian/statistical inference, and/or software development for astronomical datasets. Department Contact for Questions Songhu Wang (sw121@iu.edu) Additional
-
. Advance Bayesian and ensemble learning approaches for non-stationary temporal processes. Implement probabilistic diffusion or generative models for long-term forecasting. Collaborate closely with
-
Google Earth Engin, R, Python, and STAN (e.g., deep learning, Bayesian regression models, spatial analyses), and running analyses on a high-performance computing cluster. Demonstrated record of publishing
-
to observe next. By combining Bayesian inference, probabilistic modeling, and machine learning, the project aims to make Arctic observations more efficient, intelligent, and impactful. You will integrate field
-
. Desirable Expertise in computational fluid mechanics, broadly construed. Expertise in Bayesian methodology for optimization and experiment design. Experience with equivariant neural networks. Track record