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
- AUSTRALIAN NATIONAL UNIVERSITY (ANU)
- Australian National University
- 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
- Purdue University
- St George's University of London
- The University of Queensland
- The University of Western Australia
- UNIVERSITY OF MELBOURNE
- UNIVERSITY OF WESTERN AUSTRALIA
- University of Adelaide
- University of Glasgow
- University of Manchester
- University of Michigan
- University of South-Eastern Norway
- University of Waterloo
- Zintellect
- 20 more »
- « less
-
Field
-
, data mining, Bayesian methods, and statistical learning About Working at the Crick Our values We are bold. We make space for creative, dynamic and imaginative ideas and approaches. We’re not afraid to do
-
more novel problems. Keywords include: automatic experimental design, Bayesian inference, human-in-the-loop learning, machine teaching, privacy-preserving learning, reinforcement learning, inverse
-
modelling of climate-sensitive infectious diseases, with a particular emphasis on Bayesian hierarchical modeling using Integrated Nested Laplace Approximation (INLA). The work will contribute to ongoing
-
of current issues and future directions within the field of Active Inference, control theory or Bayesian inference. B7 Experience with building computational models of human users in an interaction setting. B8
-
.: 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
-
or a numerate discipline OR equivalent experience. Broad knowledge of probabilistic models, Bayesian inference and machine learning methods. Good knowledge of R, Python or both (links to project source
-
designs and methods, clinical trial methods, Bayesian methods, and developing R packages and scalable algorithms. Opportunities for collaboration across the Department of Biostatistics and the Medical