Sort by
Refine Your Search
-
Listed
-
Category
-
Country
-
Program
-
Employer
- Monash University
- ;
- Imperial College London
- University of Oslo
- Nature Careers
- ; University of Warwick
- ETH Zurich
- Forschungszentrum Jülich
- Heriot Watt University
- Institut Pasteur
- SciLifeLab
- Swedish University of Agricultural Sciences
- University of Glasgow
- ; University of Southampton
- ; University of Sussex
- AUSTRALIAN NATIONAL UNIVERSITY (ANU)
- Argonne
- Arizona State University
- Aston University
- Australian National University
- CEA
- Chalmers University of Technology
- DURHAM UNIVERSITY
- Durham University
- Freenome
- Integreat -Norwegian Centre for Knowledge-driven Machine Learning
- KINGS COLLEGE LONDON
- King's College London
- La Trobe University
- NIST
- Nanyang Technological University
- Purdue University
- Queensland University of Technology
- SUNY University at Buffalo
- Technical University of Denmark
- University of Adelaide
- University of Birmingham
- University of Bristol
- University of California, Los Angeles
- University of Cambridge
- University of Groningen
- University of London
- University of Manchester
- University of Miami
- University of Minnesota
- University of Sheffield
- University of Texas at Arlington
- University of Warsaw
- Western Norway University of Applied Sciences
- 39 more »
- « less
-
Field
-
more information for a given experimental budget. Efficient active learning depends on the careful co-design of experiments and inference algorithms. You will explore topics such as how to elicit
-
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
-
processes related to carbon cycling in the soil-plant system Experience with Bayesian inference and machine learning is an asset Ability to work independently and cooperatively as part of an interdisciplinary
-
Research Associate to contribute to a project focused on robust Bayesian inference with possibility theory. Robust inference is crucial for many real applications in which datasets are invariably corrupted
-
more novel problems. Keywords include: automatic experimental design, Bayesian inference, human-in-the-loop learning, machine teaching, privacy-preserving learning, reinforcement learning, inverse
-
reliability-based design optimization and hierarchical Bayesian inversion. This specific PhD position focuses on the challenges within hierarchical Bayesian inference. Job description As the successful
-
carbon, water and energy states. The successful applicant will specifically support carbon and water cycle science, applications and process model innovations using CARDAMOM-based Bayesian inference
-
models; 2. Statistical methods, analysis, and inference for large-scale computational simulator applications; 3. Uncertainty representation, quantification and propagation; and 4. Scalable data science
-
, sampling, inference, and machine learning. On one side, statistical approaches such as Bayesian inference play a critical role in identifying the parameters of PDEs, while on the other, newly emerging
-
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