Sort by
Refine Your Search
-
Listed
-
Category
-
Country
-
Employer
- Cranfield University
- Delft University of Technology (TU Delft)
- Fraunhofer-Gesellschaft
- Tallinn University of Technology
- CNRS
- Forschungszentrum Jülich
- University of Exeter
- Delft University of Technology (TU Delft); 17 Oct ’25 published
- Delft University of Technology (TU Delft); yesterday published
- ISCTE - Instituto Universitário de Lisboa
- National University of Science and Technology POLITEHNICA Bucharest
- Newcastle University
- Technical University of Denmark
- UiT The Arctic University of Norway
- University of A Coruña
- Uppsala universitet
- Alfred-Wegener-Institut Helmholtz-Zentrum für Polar- und Meeresforschung
- Cranfield University;
- DTU Electro
- Delft University of Technology (TU Delft); Delft
- Delft University of Technology (TU Delft); today published
- Drexel University
- ETH Zürich
- Edmund Mach Foundation
- Fraunhofer Institute for Wind Energy Systems IWES
- Institute of Biochemistry and Biophysics Polish Academy of Sciences
- KU LEUVEN
- Linköping University
- Linköpings universitet
- Loughborough University
- Max Planck Institute for Intelligent Systems, Tübingen site, Tübingen
- NTNU - Norwegian University of Science and Technology
- Technical University Of Denmark
- Technical University of Munich
- Universidad de Alicante
- Universidade de Vigo
- University of Adelaide
- University of Birmingham
- University of Bremen •
- University of Cambridge
- University of East Anglia
- University of Groningen
- University of Surrey;
- University of Trento
- University of Warwick
- Université Marie et Louis Pasteur
- Vrije Universiteit Brussel
- 37 more »
- « less
-
Field
-
—remains a critical challenge. This project will focus on designing AI-driven cognitive navigation solutions that can adaptively fuse multiple sensor sources under uncertainty, enabling safe and efficient
-
LEO, MEO, and GEO constellations), and complementary on-board sensors. Research will investigate algorithms for robust multi-sensor fusion and positioning assurance. A strong emphasis will be placed
-
creation of a database for the various pollution sensors with a view to training online (non-embedded) models in the first instance. - Development of a machine learning algorithm based on the study database
-
are categorised as non-destructive testing techniques, but they can be costly considering the number of sensors required and the maintenance of the data acquisition system. Hence, the alternative of direct
-
is to develop machine-learning-based algorithms for transmitter pre-distortion and receiver post-distortion architectures that enable distortion-free quantum communication systems. A key focus will be
-
sensors systems and UAVs at different scales. In particular, we will combine borehole and surface GPR as well as small-scale EMI measurements with root and shoot observations in controlled experiments
-
—remains a critical challenge. This project will focus on designing AI-driven cognitive navigation solutions that can adaptively fuse multiple sensor sources under uncertainty, enabling safe and efficient
-
to demonstrate real-world feasibility. The overarching goal is to bridge high-level algorithmic innovation with energy-aware hardware deployment, enabling intelligent sensor systems that act as autonomous micro
-
, LiDAR, AHRS, and other sensors directly on low-power embedded platforms. Where to apply E-mail resurse.umane@upb.ro Requirements Research FieldEngineering » Electronic engineeringEducation LevelMaster
-
data are needed to enhance our understanding of sources, pathways and impact of litter. Cefas is developing a visible light (VL) deep learning (DL) algorithm and collected a large 89 litter category