Sort by
Refine Your Search
-
Listed
-
Category
-
Country
-
Employer
-
Field
-
entitled “Beyond Data-Augmentation: Advancing Bayesian Inference for Stochastic Disease Transmission Models”. The overarching aim of the project is to develop the next generation of statistical tools
-
high information content Flow MRI datasets with physics based modelling and Bayesian inference to determine constitutive models for non-Newtonian and other complex fluids in situ. The project will
-
. The postholder will be based in the Center for Communicable Disease Dynamics within the Department of Epidemiology, and will be a member of the HIV Inference Group a geographically distributed and substantively
-
2028 working 21 hours per week. We are looking for a Senior Research Fellow in Statistics to advance the dynamic research portfolio of the Population Data Science group (https://popdatasci.swan.ac.uk
-
, Artificial Intelligence , Bayesian Statistics , Big Data , Scientific Machine Learning , Social Sciences , Biomedical Informatics , Causal Inference , Computational Social Science , Data Science and
-
getting Bayesian type uncertainty for parameters given data (i.e., a posterior type distribution over the parameter space) without specifying a model nor a prior. Such methods can in principle be applied
-
(“propagates”); how it varies among diseases, subtypes, and individuals; how risk factors influence mechanisms. The role holder will work within a common Bayesian inference framework enabling quantification
-
for learning about models from data, 2) incorporation of expert knowledge in model building through Bayesian prior elicitation, and 3) develop new methods for identification of conflicts in different parts
-
gravitational-wave astronomy. The successful candidate will join Greg Ashton’s STFC-funded programme, Advancing Gravitational-Wave Astronomy Using Artificial Intelligence, to work on computational Bayesian
-
radio interferometry data, particularly very long baseline interferometry. Experience with or skills relevant to statistical modelling and Bayesian inference. Demonstrated familiarity with the fields of X