Sort by
Refine Your Search
-
Listed
-
Category
-
Country
-
Employer
- University of Groningen
- ; University of Sussex
- DAAD
- Technical University of Denmark
- Leibniz
- Leiden University
- Radboud University
- CNRS
- Nature Careers
- Universiteit van Amsterdam
- ;
- ; Swansea University
- ; University of Bristol
- ; University of Oxford
- ; University of Southampton
- ; University of Warwick
- Curtin University
- Delft University of Technology (TU Delft)
- ETH Zurich
- Fraunhofer-Gesellschaft
- Inria, the French national research institute for the digital sciences
- International PhD Programme (IPP) Mainz
- Mines Paris - PSL, Centre PERSEE
- Monash University
- NTNU Norwegian University of Science and Technology
- Purdue University
- RMIT University
- TECHNISCHE UNIVERSITAT DRESDEN (TU DRESDEN)
- Technical University of Munich
- Technische Universität Berlin •
- University of Bonn •
- University of Cambridge
- University of Luxembourg
- 23 more »
- « less
-
Field
-
further research and development at Finatrax. Supported in a strong team environment the candidate will develop an innovative approach to renewable energy system modelling (based on stochastic optimization
-
approaches (e.g. SPG) as well as the use of machine learning, advanced computing, statistical modelling to explore the stochastic response to complex scenarios. This project offers the opportunity to undertake
-
lines: PGMs for decision support systems and foundations of stochastic computing. The first line focuses on aspects such as explainability, trustworthiness, maintainability, and online or federated
-
given to computationally intensive components, including ordinary and stochastic differential equations, and non-parametric components like Gaussian Processes. The project builds on the following key
-
suitable for the privacy preservation of distributed machine learning scenarios such as federated learning, split learning, and distributed stochastic gradient descent. The existing differentially private
-
, and stochastic optimization, embracing an interdisciplinary approach. The candidate will be embedded in the Explanatory Data Analysis group at the Leiden Institute of Advanced Computer Science. More
-
: (1) PGMs for decision support systems, and (2) foundations of stochastic computing. The first line focuses on explainability, trustworthiness, maintainability, online or federated learning in Bayesian
-
. Familiarity with macroeconomic dynamic stochastic general equilibrium (DSGE) models or survey design is encouraged and greatly beneficial. We are looking for ambitious, highly motivated candidates who wish
-
during training, an effect attributed to the properties of the optimization technique. Intuitively, stochastic optimizers tend to converge to flatter minima in the complex loss landscape, which is believed
-
Anemometry (LDA), and Particle Image Velocimetry (PIV). Extend existing in-house wind field models (based on stochastic differential equations such as Langevin or Fokker-Planck types). Integrate novel