Sort by
Refine Your Search
-
Listed
-
Category
-
Country
-
Program
-
Employer
- University of Oslo
- ;
- University of Glasgow
- Duke University
- Northeastern University
- Stony Brook University
- University of New South Wales
- University of Sheffield
- ; University of Exeter
- Baylor College of Medicine
- Boston University
- Case Western Reserve University
- DURHAM UNIVERSITY
- Dalhousie University
- Durham University
- Florida International University
- Georgetown University
- Indian Institute of Science Education & Research Thiruvananthapuram
- Institut Pasteur
- Integreat -Norwegian Centre for Knowledge-driven Machine Learning
- King Abdullah University of Science and Technology
- Monash University
- Nanyang Technological University
- Purdue University
- Queensland University of Technology
- RMIT University
- Rutgers University
- Technical University of Denmark
- University of Birmingham
- University of Cambridge
- University of Houston Central Campus
- University of London
- University of Lund
- University of South-Eastern Norway
- University of Toronto
- University of Utah
- Zintellect
- 27 more »
- « less
-
Field
-
Processing/Control Path Planning/Trajectory Planning Multi-Target Tracking/Multi-Object Tracking, Bayesian Filtering, Radom Finite Set filters or closely related multi-target tracking approaches in radar
-
tasks require high-frequency evaluations of forward models, in order to quantify the uncertainties of rock and fluid properties in the subsurface formations. Therefore, the objectives of this research
-
year round Details This research is aimed at developing scalable Bayesian approaches able to solve complex and high dimensional problems with multiple objects and multi-sensor data. One such problem is
-
Expertise in quantitative modeling, computational and/or Bayesian methods Expertise using at least one programming languages in the analysis of scientific data such as R, Python, Matlab, or Julia. Expertise
-
challenging data problem. Weak signals from collisions of compact objects can be dug out of noisy time series because we understand what the signal should look like, and can therefore use simple algorithms