Sort by
Refine Your Search
-
Listed
-
Country
-
Program
-
Employer
- UNIVERSITY OF SOUTHAMPTON
- University of Oslo
- Aalborg University
- CNRS
- Indiana University
- Montana State University
- National University of Singapore
- Nature Careers
- University of Amsterdam (UvA)
- Aalborg Universitet
- Cornell University
- Inria, the French national research institute for the digital sciences
- Institut de Físiques d'Altes Energies (IFAE)
- Johns Hopkins University
- KTH Royal Institute of Technology
- Lawrence Berkeley National Laboratory
- Massachusetts Institute of Technology (MIT)
- Max Planck Institute for Physics, Garching
- Newcastle University;
- Queen's University
- RIKEN
- Technical University of Denmark
- The University of Southampton
- UNIVERSITY OF HELSINKI
- Universidad Politécnica de Madrid
- University of Amsterdam (UvA); Published yesterday
- University of Amsterdam (UvA); yesterday published
- University of Bristol
- University of Bristol;
- University of Leeds
- University of Leeds;
- University of London
- University of Manchester
- University of Massachusetts Medical School
- University of Michigan
- University of Minnesota
- University of Oxford
- University of Southampton;
- 28 more »
- « less
-
Field
-
, geospatial statistics, Bayesian statistics, burden mapping, measuring the impact of the environment on disease among others. The PI has projects in both infectious and chronic disease, measuring the impact of
-
development and statistical modelling in resilience assessment (e.g., dynamic/latent-variable models, Bayesian hierarchical models, causal inference, time-series analysis, cognitive modelling) Build robust
-
include, but not limited to, computational approaches such as AI and machine learning; methodological foundations and computational approaches for AI for biomedicine, Bayesian inference, cancer imaging
-
Description Distribution estimation algorithms for abductive inference (total or partial) in dynamic domains. Structural learning of dynamic Bayesian networks with discrete and continuous variables (parametric
-
Exactly: A Bayesian Approach. The project aims to address the challenges in pooling inference, by developing and implementing either exact or asymptotically exact Monte Carlo algorithms in collaboration
-
to the development of Bayesian inference frameworks that use GATES. What will you be doing? The postholder will develop machine learning models of atmospheric transport and use them in Bayesian inverse modelling
-
the mismodeling of gravitational waves, of astrophysical environments, or of noise artifacts in gravitational-wave inference, The development of Bayesian data analysis techniques to carry out parameter estimation
-
to the development of Bayesian inference frameworks that use GATES. The postholder will develop machine learning models of atmospheric transport and use them in Bayesian inverse modelling frameworks to estimate
-
, and lineage-specific dynamics. Assess congruence and robustness of phylogenetic reconstructions using Bayesian inference, parsimony, and tip-dating, and evaluate their impact on macroevolutionary
-
the mismodeling of gravitational waves, of astrophysical environments, or of noise artifacts in gravitational-wave inference, The development of Bayesian data analysis techniques to carry out parameter estimation