Sort by
Refine Your Search
-
Listed
-
Category
-
Country
-
Employer
- CNRS
- Technical University of Denmark
- University of Oslo
- Technical University of Munich
- Utrecht University
- ;
- BRGM
- Bielefeld University
- Centrale Supelec
- Chalmers University of Technology
- DAAD
- Ecole Centrale de Lyon
- FCiências.ID
- Forschungszentrum Jülich
- Fraunhofer-Gesellschaft
- French National Research Institute for Agriculture, Food and Environment (INRAE)
- INRIA
- Inria, the French national research institute for the digital sciences
- Linköping University
- Ludwig-Maximilians-Universität München •
- Maastricht University (UM)
- Nature Careers
- Newcastle University
- Radboud University
- Swedish University of Agricultural Sciences
- Technical University Of Denmark
- The University of Manchester
- UCL
- Umeå University
- University of Amsterdam (UvA)
- University of Bologna
- University of Surrey
- University of Texas at El Paso
- Uppsala universitet
- Vrije Universiteit Amsterdam (VU)
- 25 more »
- « less
-
Field
-
related to Riemann-Steltjes optimal control to combine PMP with Bayesian Optimisation, allowing for data-efficient learning. You will then implement and validate the new method on simulated fermentations
-
description The position is connected to the project “Bayesian Enhanced Tensor Factorization Embedding Structure (BETTER)”, and this PhD project specifically aims at developing a unified, scalable, and
-
medical applications. Federated Bayesian learning offers a solution to those problems by allowing multiple participants to train machine learning models collaboratively, without sharing any data. Bayesian
-
connected to the project “Bayesian Enhanced Tensor Factorization Embedding Structure (BETTER)”, and this PhD project specifically aims at developing a unified, scalable, and interpretable framework for tensor
-
. Bayesian networks and related machine-learning methods will be used to calculate cross-section probability density functions in a much faster way, enabling the combination of multiple probability
-
of error-controlled biomechanical models in SOFA / FEniCSx / SOniCS for real-time use on AR devices Design of Bayesian neural-network surrogates and graph-based models for tissue deformation and brain shift
-
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
-
Your Job: This research primarily seeks to incorporate advanced neuron models, such as those capturing dendritic computation and probabilistic Bayesian network behavior, into unconventional
-
of working cooperative and team-orientated working style Specific Requirements experience with research in teams knowledge of the fundamentals of Bayesian statistics knowledge of the fundamentals of radio
-
insights that inform biodiversity management. The project includes: · Apply of deep learning models to annotate bird and bat species from sound recordings. · Develop a Bayesian statistical