Sort by
Refine Your Search
-
Listed
-
Category
-
Country
-
Employer
- Montana State University
- UiT The Arctic University of Norway
- Cornell University
- National University of Singapore
- Queen's University
- The University of Southampton
- UNIVERSITY OF HELSINKI
- UNIVERSITY OF SOUTHAMPTON
- University of Bristol
- University of Bristol;
- University of Leeds
- University of Leeds;
- University of London
- University of Manchester
- University of Michigan
- 5 more »
- « less
-
Field
-
Gravitational-Wave Astronomy Using Artificial Intelligence, to work on computational Bayesian inference methods and their astrophysical applications. Southampton's School of Mathematical Sciences is home to a
-
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
-
, 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
-
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. What will you be doing? The postholder will develop machine learning models of atmospheric transport and use them in Bayesian inverse modelling
-
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
-
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 address more novel problems. Keywords include: automatic experimental design, Bayesian inference, human-in-the-loop learning, machine teaching, privacy-preserving learning, reinforcement learning, inverse
-
and conducting climate model simulations and analysing large volumes of ESM simulations. Knowledge of reduced-order modelling and Bayesian inference is highly valued, and experience with climate
-
, New York 14850, United States of America [map ] Subject Areas: Data Science / Statistics , Applied Mathematics , Artificial Intelligence , Bayesian Statistics , Big Data , Scientific Machine Learning