Sort by
Refine Your Search
-
Listed
-
Country
-
Program
-
Employer
- Cranfield University
- Newcastle University
- Forschungszentrum Jülich
- Institute of Biochemistry and Biophysics Polish Academy of Sciences
- Norwegian University of Life Sciences (NMBU)
- Swansea University
- University of Amsterdam (UvA)
- Centre for Genomic Regulation
- Cranfield University;
- DAAD
- Erasmus University Rotterdam
- Linköpings universitet
- Loughborough University
- Manchester Metropolitan University
- National Research Council Canada
- Nature Careers
- Newcastle University;
- Technical University of Denmark
- Technical University of Munich
- Umeå University
- Universidade do Minho - ISISE
- University of Adelaide
- University of Amsterdam (UvA); Published today
- University of Exeter;
- University of Southern Denmark
- University of Surrey
- Università di Pisa
- Vrije Universiteit Brussel
- 18 more »
- « less
-
Field
-
strong foundation in programming (e.g. Python) and core concepts in machine learning or data analysis. Ability to engage with research literature and develop analytical, problem-solving, and algorithmic
-
algorithms and methods for calibrated Bayesian federated learning for trustworthy collaborative Bayesian learning on data from multiple participants. The project will develop new methods, theory, and
-
emerged to make meshing more flexible by allowing elements to span across multiple CAD faces without explicitly modifying the geometry. However, these ideas have not yet been developed in high-order
-
actors. The developed algorithms will be validated using simulation testbeds and simple hardware-in-the-loop microgrid setups with battery storage. Overall, this research will advance the state of the art
-
cryptography, collaborating with fellow researchers. Lead the design, implementation, and evaluation of innovative quantum-safe cryptographic algorithms and protocols. Lead the development of a post-quantum
-
are poised to re-define our future mobility. However, full autonomy is not possible without all-weather perception for which Radar sensing/imaging is essential. This project focuses on developing algorithms
-
into smaller, faster, more energy efficient and cost-effective hardware compared to the current state-of-the-art. The project will align the in-house algorithm-to-hardware development of the Micro-Systems
-
compared to the current state-of-the-art. The project will align the in-house algorithm-to-hardware development of the Micro-Systems Research Group at Newcastle University with next-generation Field
-
(rhizotron facility) and field trials. In addition to field applications, novel inversion algorithms for ground-penetrating radar (GPR) and electromagnetic (EM) will be developed. These algorithms will enable
-
allowing elements to span across multiple CAD faces without explicitly modifying the geometry. However, these ideas have not yet been developed in high-order settings, where curved elements, geometric