Sort by
Refine Your Search
-
Listed
-
Category
-
Country
-
Employer
- ;
- DAAD
- Nature Careers
- Technical University of Munich
- SciLifeLab
- ; University of Bristol
- CWI
- Chalmers University of Technology
- Cranfield University
- Curtin University
- Helmholtz-Zentrum Geesthacht
- Technical University of Denmark
- University of Groningen
- University of Nottingham
- Vrije Universiteit Brussel
- ; Max Planck Institute for Psycholinguistics
- ; The University of Edinburgh
- ; University of Essex
- ; University of Nottingham
- Aalborg University
- Duke University
- Fraunhofer-Gesellschaft
- Ghent University
- Institut Pasteur
- Monash University
- NTNU - Norwegian University of Science and Technology
- Radboud University
- The Max Planck Institute for Neurobiology of Behavior – caesar •
- Umeå University
- Universiteit van Amsterdam
- University of Adelaide
- University of California Irvine
- University of Cambridge
- University of Göttingen •
- University of Minnesota
- University of Twente
- University of Tübingen
- 27 more »
- « less
-
Field
-
-edge solutions and pushing the boundaries in the field Develop advanced artificial neural networks (ANN), including training, mapping, and weight quantization Collaborate with cross-functional teams
-
-informed data analytics tools for the predictive maintenance (PdM) strategy applications to high-value critical assets. Among others, the recently developed Physics-informed Neural Network (PINN) technique
-
are developed, modelled and controlled. You will create novel adaptative, physics-informed models that tightly integrate thermo-fluid dynamic laws, deep learning neural networks, and experimental data. A key
-
to machine learning and deep neural networks, into the DG finite element solver to reduce computational costs while maintaining the accuracy. The key objective of this work will be to provide step-change
-
interested in conducting mathematical research that combines its theoretical rigor and beauty with real-world applications, including climate and weather, the formation of opinions and neural networks
-
. The School comprises of four Research Groups, which are: Artificial Intelligence Brain Computer Interfaces and Neural Engineering Communications and Networks Robotics and Embedded Systems Research within
-
. This project will rely on recent advances in neural networks to develop machine learning potentials (MLPs) for MD simulations of realistic nanomaterial/coolant-liquids and use these to gain fundamental insights
-
Martin Australia invite applications for a project under this program, exploring the development of Physics Informed Neural Networks (PINNs) for efficient signal modelling in areas such as weather
-
in probabilistic AI. This initiative is motivated by the observation that many fundamental problems in AI could benefit from expertise in these disciplines. These include understanding deep neural
-
and prosthetic devices in the real-world. This PhD project offers the opportunity to work on pioneering research that combines state of the art computational modelling (deep neural networks) and