Sort by
Refine Your Search
-
Listed
-
Category
-
Country
-
Program
-
Employer
- Monash University
- Zintellect
- King Abdullah University of Science and Technology
- Oak Ridge National Laboratory
- SINGAPORE INSTITUTE OF TECHNOLOGY (SIT)
- University of Glasgow
- University of Oslo
- European Space Agency
- Indiana University
- Inria, the French national research institute for the digital sciences
- Lancaster University
- NTNU - Norwegian University of Science and Technology
- SUNY University at Buffalo
- University of California
- University of Michigan
- University of Sheffield
- Aalborg University
- Aarhus University
- Boston University
- Brandeis University
- Brookhaven National Laboratory
- California Institute of Technology
- Cornell University
- DIFFER
- Delft University of Technology (TU Delft)
- EPFL
- Ghent University
- Harvard University
- IIASA
- INAF-Osservatorio Astronomico di Trieste
- INSERM
- Institut national de l'information géographique et forestière IGN
- Instituto de Ciência e Inovação em Engenharia Mecânica e Engenharia Industrial
- KU LEUVEN
- Loyola University
- Ludwig-Maximilians-Universität München •
- MOHAMMED VI POLYTECHNIC UNIVERSITY
- Macquarie University
- Max Planck Institutes
- Missouri University of Science and Technology
- National University of Singapore
- Norwegian University of Life Sciences (NMBU)
- Sandia National Laboratories
- SciLifeLab
- Simons Foundation/Flatiron Institute
- Technical University of Munich
- Texas A&m Engineering
- UNIVERSITE DE TECHNOLOGIE DE COMPIEGNE
- University of Cambridge;
- University of Idaho
- University of Nottingham
- University of Surrey
- University of Texas at El Paso
- University of Toronto
- University of Vienna
- Université de Bordeaux / University of Bordeaux
- 46 more »
- « less
-
Field
-
on Bayesian small area estimation (SAE) methods that borrow statistical strength across space and time when local data are insufficient. Building on the Fay-Herriot model and its spatiotemporal extensions
-
discovery. Bayesian approaches provide a principled framework for modeling uncertainty by capturing posterior distributions over model parameters or predictions. Despite recent progress in approximate
-
and research in several areas. These include, but are not limited to: Adversarial location and network interdiction models Adversarial machine learning attacks and defense (e.g., against Bayesian
-
, using Hubble Space Telescope (HST) images as input into radiative transfer models. The ultimate objective is to calculate an improved estimate of the Bond albedo of Uranus, with well characterized
-
existing studies, lake model simulations for emulator development and calibration. Use the emulator in a Bayesian statistical framework to quantitatively interpret paleoclimate proxy time series. Lead
-
The Statistics (STAT) program in the Computer, Electrical, and Mathematical Sciences and Engineering Division (https://cemse.kaust.edu.sa ) at King Abdullah University of Science and Technology
-
Computer Science, Robotics, Systems Engineering, Electrical and Computer Engineering, Mechanical Engineering, Chemical Engineering, Materials Science & Engineering, Chemistry, or a related quantitative scientific
-
spatial meteorological data. Models GPS location data of animals to estimate movement behavior. Develops statistical models (especially Bayesian hierarchical models) of wildlife disease surveillance data
-
qualifications Publications at top machine learning or computer vision conferences (NeurIPS, ICML, ICLR, CVPR, AISTATS etc.) are highly meriting. Expertise in Bayesian methods, generative models, multimodal models
-
-time, and evidence-accumulation phenomena. Implement simulation, parameter-estimation, and model-comparison methods in Python, MATLAB, R, or related computational environments. Lead and co-author