Sort by
Refine Your Search
-
Listed
-
Country
-
Program
-
Employer
- Imperial College London
- University of Oslo
- ;
- Heriot Watt University
- Nature Careers
- Swedish University of Agricultural Sciences
- AUSTRALIAN NATIONAL UNIVERSITY (ANU)
- Argonne
- Arizona State University
- Aston University
- Australian National University
- CEA
- Chalmers University of Technology
- DURHAM UNIVERSITY
- Durham University
- Integreat -Norwegian Centre for Knowledge-driven Machine Learning
- La Trobe University
- Monash University
- Purdue University
- SciLifeLab
- University of Adelaide
- University of Birmingham
- University of London
- University of Manchester
- University of Minnesota
- Western Norway University of Applied Sciences
- 16 more »
- « less
-
Field
-
, approximate inference, deep learning, or Bayesian optimisation are encouraged to apply. Interpretable Machine Learning for Natural Language – Led by Prof Lexing Xie, this stream applies machine learning
-
, large-grant project on the epidemiology of bovine tuberculosis in wild badgers, using state-of-the-art Bayesian modelling approaches to study the drivers of infectiousness and transmission of infection in
-
AUSTRALIAN NATIONAL UNIVERSITY (ANU) | Canberra, Australian Capital Territory | Australia | about 6 hours ago
deep learning theory and practice. Applicants with expertise in probabilistic modelling, approximate inference, deep learning, or Bayesian optimisation are encouraged to apply. Interpretable Machine
-
experiments. The objective is to develop Bayesian causal models and neural networks capable of identifying relevant causal relationships between instrumental parameters and observed anomalies. The work will
-
techniques from statistical physics, Bayesian inference, and complex systems theory to address challenges posed by noisy and incomplete data. Depending on the results obtained in the first year, the post can
-
more novel problems. Keywords include: automatic experimental design, Bayesian inference, human-in-the-loop learning, machine teaching, privacy-preserving learning, reinforcement learning, inverse
-
models; 2. Statistical methods, analysis, and inference for large-scale computational simulator applications; 3. Uncertainty representation, quantification and propagation; and 4. Scalable data science
-
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 Inference and Robotics
-
processes, Bayesian inference, signal models, sampling theory, sensing techniques, optimisation theory and algorithms, multi-modal data processing, high-performance computing, mathematical image analysis
-
interpreted very broadly, e.g.: topics in Bayesian Inference and Robotics; ‘Science’ covers any typical topic in Natural Science and Engineering (Epidemiology, Biology and basic science in biomedicine