Sort by
Refine Your Search
-
Listed
-
Category
-
Country
-
Employer
- Imperial College London
- ;
- Nanyang Technological University
- University of Oslo
- Nature Careers
- University of Alabama, Tuscaloosa
- University of Bergen
- University of Birmingham
- City of Hope
- Dalhousie University
- Integreat -Norwegian Centre for Knowledge-driven Machine Learning
- King Abdullah University of Science and Technology
- La Trobe University
- Max Planck Institutes
- Monash University
- Montana State University
- Purdue University
- St George's University of London
- The University of Queensland
- The University of Western Australia
- Tufts University
- UNIVERSITY OF MELBOURNE
- UNIVERSITY OF WESTERN AUSTRALIA
- University of Adelaide
- University of Manchester
- University of Michigan
- University of South-Eastern Norway
- University of Waterloo
- Western Norway University of Applied Sciences
- Zintellect
- 20 more »
- « less
-
Field
-
. Probabilistic rational models, implemented as either Bayesian models or deep neural networks, have been proposed as standard models, from low-level perception and neuroscience to cognition and economics. But
-
processing would be an advantage. Proficiency in statistical software (e.g., R, Python, SAS, or Stata). Experience with clinical informatics approaches (e.g., cluster analysis, machine learning, Bayesian
-
research interests in one or more of the following subfields: scientific machine learning, optimization, deep learning, uncertainty quantification, (Bayesian) inverse problems, reduced order modeling, high
-
computational modeling, geometric morphometrics, multivariate and Bayesian statistics, spatiotemporal and spatial modeling (including GIS), causal inference, machine learning, AI, and statistical software
-
/drawbacks. Experience with Bayesian statistics a plus. Experience with censored datasets a plus. Proven record in writing successful research proposals. Demonstrated ability of working in a multidisciplinary
-
), clinical trials, disease surveillance, and the use of novel methods including Bayesian network, machine learning, social network analysis and dynamic data visualisation tools. Further information is
-
approaches. Experience in programming in R, using GitHub, and doing Bayesian statistical analyses with the use of MCMC samplers such as JAGS, STAN, or NIMBLE. Point of Contact Justina Eligibility Requirements
-
, Bayesian modeling, and/or statistical machine learning. The Successful Candidate Will Ability to cooperate with other researchers and be an effective team player. Excellent written and oral
-
Elhoseiny, Code: https://github.com/yli1/CLCL Uncertainty-guided Continual Learning with Bayesian Neural Networks (ICLR’20), Sayna Ebrahimi, Mohamed Elhoseiny, Trevor Darrell, Marcus Rohrbach, Code: https
-
informatics approaches (e.g., machine learning, Bayesian statistics) and spatial data processing and analysis skills would be of advantage. Expertise in Stata, R, or other analytic tools. Strong communication