Sort by
Refine Your Search
-
Listed
-
Category
-
Country
-
Employer
- Technical University of Munich
- ; The University of Manchester
- ;
- ; Technical University of Denmark
- ; University of Birmingham
- ; University of Cambridge
- ; University of Sheffield
- ; University of Warwick
- Aalborg University
- Aarhus University
- Blekinge Institute of Technology
- CSIRO
- Cranfield University
- Fraunhofer-Gesellschaft
- Ghent University
- Imperial College London
- Lulea University of Technology
- NTNU - Norwegian University of Science and Technology
- Technical University of Denmark
- University of Adelaide
- University of Antwerp
- University of Cambridge
- University of Groningen
- Uppsala University
- Wageningen University and Research Center
- 15 more »
- « less
-
Field
-
. This project seeks to advance energy autonomy by optimising power conversion, storage, and distribution in such systems, enabling broader adoption in real-world applications. The project aims to develop a PMC
-
Research and Logistics group at Wageningen University, the Zero Hunger Lab at Tilburg University, and four industry partners. In this project, you will develop and advance optimization models and algorithms
-
optimisation algorithms to dynamically reconfigure the substation/distribution network settings to enhance the system efficiency. The optimisation algorithms will incorporate the uncertainties associated with
-
of nanofabricated sample supports and tracking algorithms for 5D electron diffraction (5DED). EMAT is one of the leading electron microscopy centers in the world and has a vast expertise in both fundamental and
-
PhD Stipends within Distributed, Embedded and Intelligent Systems (DEIS) At the Technical Faculty of IT and Design, Department of Computer Science, one PhD stipend is available within
-
achieve automated data driven optimization (in terms of time and quality) of polishing process parameters by application of machine learning algorithms, leading to a robust, repeatable and fast polishing
-
in Adelaide and Melbourne. Expected outcomes The Finite Element Method (FEM) is the current dominant approach for modelling real-world signals but requires substantial, uniformly distributed data. Real
-
better and faster decisions when assessing funding applications, ensuring the efficient and unbiased elimination of poor applications? This question can be addressed through training algorithms on past
-
and application architectures with implementations of massively distributed embedded systems that interact with each other and their environment to enable secure, goal-driven, autonomous and evolvable
-
are using ferroelectric memories, which can calculate AI algorithms from the field of deep learning in resistive crossbar structures with extremely low power consumption and high speed. Furthermore, we