Sort by
Refine Your Search
-
Listed
-
Category
-
Country
-
Employer
- Chalmers University of Technology
- Technical University of Denmark
- Technical University of Munich
- Bielefeld University
- CNRS
- Centrale Supelec
- DAAD
- Ecole Centrale de Lyon
- FCiências.ID
- Forschungszentrum Jülich
- Helmholtz-Zentrum Geesthacht
- Inria, the French national research institute for the digital sciences
- LInköpings universitet
- Linköping University
- Ludwig-Maximilians-Universität München •
- Maastricht University (UM)
- Swedish University of Agricultural Sciences
- Technical University Of Denmark
- The Belgian Nuclear Research Centre
- UCL
- University of Amsterdam (UvA)
- University of Berne, Institute of Cell Biology
- University of Bologna
- University of Texas at El Paso
- Utrecht University
- Vrije Universiteit Amsterdam (VU)
- 16 more »
- « less
-
Field
-
programming, Bayesian deep learning, causal inference, reinforcement learning, graph neural networks, and geometric deep learning. In particular, you will be part of the Causality team under the supervision
-
will combine digital twins based on established process designs and process engineering fundamentals with data-driven optimisation techniques, specifically Bayesian statistics and Bayesian optimisation
-
lattice orientation by EBSD or local chemical composition by EDX [1]. For instance, an original protocol based on Bayesian inference was recently co-developed by LEM3 and ICA to determine the single-crystal
-
complemented with a supersonic module grounded on the work of Bufi & Cinnella (link to the research paper). In a second step, using Bayesian processes (Lam et al., 2015) and new acquisition functions
-
equalizer (DFE) and a channel decoder based on PGMs and BP. The proposed research project aims to explore when and how combinedGNNs and PGMs can improve Bayesian receiver design and beamforming for multiuser
-
science, or public health. Is proficient in modern statistical modelling, AI & machine learning methods (e.g. system identification, regression models, Bayesian methods, deep learning). Is an experienced
-
, please visit: https://qbm.genzentrum.lmu.de/application/ Tuition fees per semester in EUR None Combined Master's degree / PhD programme No Joint degree / double degree programme No Description/content
-
equations. Your main research assignments will be to develop new models and methods for generative sampling and Bayesian inference. You will be jointly supervised by Assistant Prof. Zheng Zhao (https
-
restoration ecology (see https://www.slu.se/en/about-slu/organisation/departments/department-of-wildlife-fish-and-environmental-studies/ ). The department has many international employees and well-established
-
Your Job: This research primarily seeks to incorporate advanced neuron models, such as those capturing dendritic computation and probabilistic Bayesian network behavior, into unconventional