Sort by
Refine Your Search
-
Listed
-
Country
-
Employer
- Nature Careers
- Indiana University
- Technical University of Denmark
- Tilburg University
- Argonne
- Ghent University
- Inria, the French national research institute for the digital sciences
- Institut de Físiques d'Altes Energies (IFAE)
- King Abdullah University of Science and Technology
- Lehigh University
- Oak Ridge National Laboratory
- The University of South Dakota
- Umeå University
- Umeå universitet
- University of Idaho
- University of Minnesota
- University of Split, Faculty of civil engineering, architecture and geodesy
- University of Tübingen
- University of Washington
- Utrecht University
- 10 more »
- « less
-
Field
-
, methodologies, and information derived from Bayesian modeling, data science, cognitive science, and risk analysis. Its primary objective is to create advanced forecasting models, generate meaningful indicators
-
of the research project “Unreal engines — Understanding language models through resource-optimal analysis: Implicit Bayesian pragmatic reasoning & emergent causal world models”. The project uses
-
areas Biomedical applications, social determinants of health or other demographic health areas Spatial microsimulation, spatially weighted regression, combinatorial optimization or Bayesian network
-
study examining common elements in decisions across different contexts (risk, uncertainty, time; gains, losses, and mixed domain choices). Applying Bayesian techniques to develop stochastic models
-
areas will be considered when selecting candidates: Machine Learning, Neural Networks, Numerical solutions of Partial Differential Equations and Stochastic Differential Equations, Numerical Optimization
-
and implement Bayesian graph neural networks and convolutional neural networks as surrogates for high-fidelity biomechanical models Quantify and propagate uncertainty, and develop strategies for model
-
model classifiers (PLS-DA, random forest, neural network, etc) towards unraveling materials structure-function relationships, and are familiar with optimization approaches such as genetic search, Bayesian
-
opportunities for collaboration with Michigan State University, and IU’s network in cognitive modeling, AI, and human–AI decision research. This postdoctoral appointment is full-time and on-campus. Job Duties 80
-
for screening purposes and cell-based therapies. We will develop methods for modelling missing not at random (MNAR) observations and quantifying uncertainty using Bayesian methods and deep learning architectures
-
sciences Strong background in deep learning, with experience in probabilistic models (e.g., Variational Autoencoders, Bayesian approaches) Proficient Python programming for machine learning and scientific