Sort by
Refine Your Search
-
Employer
- Oak Ridge National Laboratory
- Argonne
- Duke University
- Indiana University
- Pennsylvania State University
- SUNY University at Buffalo
- Boston University
- Brookhaven National Laboratory
- Cornell University
- Harvard University
- Lehigh University
- Missouri University of Science and Technology
- New York University
- Northeastern University
- Rutgers University
- Sandia National Laboratories
- Texas A&M University
- Texas A&m Engineering
- University of Connecticut
- University of Idaho
- University of Maryland Baltimore County
- University of Minnesota
- University of Virginia
- University of Washington
- Virginia Tech
- 15 more »
- « less
-
Field
-
via nonlinear parametrizations such as deep networks, dynamical systems and control, Bayesian inference and generative modeling, and randomized linear algebra. Applications of interest are transport
-
device-relevant properties Design active learning, Bayesian optimization, uncertainty-aware modeling, and other adaptive experimental design workflows to guide experiments and improve data efficiency in
-
. Preferred Qualifications: Knowledge of Approximate, Local, Rényi, Bayesian differential privacy, and other related definitions. Knowledge of federated learning SOTA algorithms. Knowledge of distributed
-
, including active learning or Bayesian optimization. Experience with imaging, time-series or high-dimensional data. Exposure to crystallography or structural biology. Experience with multimodal datasets and
-
Missouri University of Science and Technology | Rolla, Missouri | United States | about 2 months ago
to have experience in several of the following areas: data processing, statistical analyses, R software, regression models, process-based models such as DSSAT or APSIM, Bayesian statistical analysis
-
to obtain and maintain a DOE Q clearance. Qualifications We Desire: Interest in developing neural-inspired and cutting-edge artificial intelligence algorithms (e.g., spiking neural networks, Bayesian neural
-
transcriptomics. He is the lead developer of widely used open-source software including BayesPrism, a Bayesian deconvolution framework selected as a Nature Cancer 2022 highlight. Dr. Chu's research has been
-
. Proficiency in Python, MATLAB, or R. Strong quantitative and analytic skills. Preferred Qualifications Experience with evidence-accumulation models (DDM, sequential sampling, Bayesian models). Experience with
-
that combines mechanistic ecophysiology with AI, such as: Physics-informed machine learning and neutral networks to investigate plant physiological / abiotic relationships Bayesian statistics and neural and
-
areas Biomedical applications, social determinants of health or other demographic health areas Spatial microsimulation, spatially weighted regression, combinatorial optimization or Bayesian network