Sort by
Refine Your Search
-
Country
-
Employer
- Imperial College London
- University of Oslo
- ;
- AUSTRALIAN NATIONAL UNIVERSITY (ANU)
- Australian National University
- Integreat -Norwegian Centre for Knowledge-driven Machine Learning
- La Trobe University
- Monash University
- Nature Careers
- Purdue University
- University of Adelaide
- University of Birmingham
- University of Glasgow
- University of Manchester
- 4 more »
- « less
-
Field
-
for Bayesian inference Documented experience with programming in either Python or R. Foreign completed degree (M.Sc.-level) corresponding to a minimum of four years in the Norwegian educational system Fluent
-
.: topics in Bayesian Inference and Robotics; ‘Science’ covers any typical topic in Natural Science and Engineering (Epidemiology, Biology and basic science in biomedicine are included but clinical medical
-
.: topics in Bayesian Inference and Robotics; ‘Science’ covers any typical topic in Natural Science and Engineering (Epidemiology, Biology and basic science in biomedicine are included but clinical medical
-
fellowships have the aim of identifying excellent researchers and accelerating them in using AI to advance and disrupt Science or Engineering. Here ‘AI’ is interpreted very broadly, e.g.: topics in Bayesian
-
fellowships have the aim of identifying excellent researchers and accelerating them in using AI to advance and disrupt Science or Engineering. Here ‘AI’ is interpreted very broadly, e.g.: topics in Bayesian
-
. Documented experience with Bayesian spatiotemporal modelling, including experience with the INLA framework for Bayesian inference Documented experience with programming in either Python or R. Foreign completed
-
and familiarity with Bayesian Inference and Markov chain Monte Carlo. Please upload your CV, a cover letter (maximum 2 pages) and names and emails of three contactable referees. The School
-
the timing, scale, and rate of mammal declines in Australia. They will use critical inferences of past demographic change and high-performance computing to disentangle the ecological mechanisms that were
-
computational modeling, geometric morphometrics, multivariate and Bayesian statistics, spatiotemporal and spatial modeling (including GIS), causal inference, machine learning, AI, and statistical software
-
position We welcome applicants with a strong background in machine learning, causal inference, and statistics, who are eager to contribute to cutting-edge research at the intersection of these fields