Sort by
Refine Your Search
-
Listed
-
Category
-
Country
-
Employer
- NEW YORK UNIVERSITY ABU DHABI
- Nature Careers
- University of Oxford
- ;
- Argonne
- SciLifeLab
- Stony Brook University
- Westlake University
- CEA
- CWI
- Cornell University
- Forschungszentrum Jülich
- Fraunhofer-Gesellschaft
- Heriot Watt University
- Linköping University
- Los Alamos National Laboratory
- MOHAMMED VI POLYTECHNIC UNIVERSITY
- New York University
- SUNY University at Buffalo
- Swedish University of Agricultural Sciences
- Technical University of Denmark
- Technical University of Munich
- Texas A&M University
- University of Florida
- University of Oslo
- University of Tübingen
- VIB
- 17 more »
- « less
-
Field
-
, Probabilistic Inference, Algebraic Topology and Wavelet analysis theory. Familiar with Matlab/Python/C++ programming. Experience with Pytorch and multi-GPU model deployment. Experience in analyzing complex
-
fundamental algorithms for producing policies for rich goal structures in MDPs (e.g. risk, temporal logic, or probabilistic objectives), and modelling robot decision problems using MDPs (e.g. human-robot
-
live in. Your role Research related to the following areas: Mathematical statistics, Machine Learning, High-dimensional statistics, Robust estimation methods, Probabilistic foundations of mathematical
-
, probabilistic models Representation learning, self-supervised learning, foundation models Data analysis, non-linear statistics, knowledge management Your profile PhD in Computer Science, Bioinformatics
-
using probabilistic methods. You will collaborate with domain experts across transport modeling, machine-learning, and policy design to ensure scientific and practical relevance. You will contribute
-
Theory, Mechanism Design, Markov Decision Processes, probabilistic analysis, stochastic games, fair division etc. and writing and disseminating the work conducted in the context of the project. A full list
-
. We envision a research paradigm shift in fluid mechanics to a physics-informed (and -informative) probabilistic learning framework, which leads to disruptive technology transformation in the aerospace
-
. We envision a research paradigm shift in fluid mechanics to a physics-informed (and -informative) probabilistic learning framework, which leads to disruptive technology transformation in the aerospace
-
sequencing, and with computer scientists at KTH in Stockholm, focused on developing scalable probabilistic machine learning techniques for online phylogenomic analysis and placement of DNA barcodes. You will
-
the development team of TreePPL (www.treeppl.org ), a universal probabilistic programming language and new software for phylogenetics. The successful candidate will be responsible for the user-interface aspects