Sort by
Refine Your Search
-
Listed
-
Category
-
Country
-
Employer
- Delft University of Technology (TU Delft)
- University of Bergen
- Cranfield University
- Fraunhofer-Gesellschaft
- University of Exeter
- Delft University of Technology (TU Delft); 17 Oct ’25 published
- Forschungszentrum Jülich
- Loughborough University
- 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
- Aalborg Universitet
- Aalborg University
- Cranfield University;
- DTU Electro
- Delft University of Technology (TU Delft); today published
- Delft University of Technology (TU Delft); yesterday published
- ETH Zürich
- Eindhoven University of Technology (TU/e)
- Fraunhofer Institute for Wind Energy Systems IWES
- ISCTE - Instituto Universitário de Lisboa
- Institute of Biochemistry and Biophysics Polish Academy of Sciences
- KU LEUVEN
- Linköping University
- Linköpings universitet
- Manchester Metropolitan 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
- Universidade de Vigo
- University of Adelaide
- University of Birmingham
- University of Bremen •
- University of Cambridge
- University of East Anglia
- University of Surrey;
- University of Warwick
- Université Marie et Louis Pasteur
- Vrije Universiteit Brussel
- 33 more »
- « less
-
Field
-
Disse), the Chair of Geoinformatics (Prof. Thomas H. Kolbe), and the Chair of Algorithmic Machine Learning & Explainable AI (Prof. Stefan Bauer). The project aims to develop an integrated urban flood
-
spanning design, modelling and simulation of photonic systems, sensor systems, signal processing and device manufacturing, development of machine learning algorithms, and design of optical communication
-
skills and motivation to implement algorithms and test them in practice on large-scale problems. Programming Skills: You are proficient in at least one scientific programming language (such as Python
-
-house database of experimental real-world data enabling large-scale validation of developed algorithms. Wind turbine drivetrains are critical components, and their failures can lead to significant
-
electrical power, enabling smart sensors to operate without batteries. You will explore novel capacitor-based rectifier architectures, adaptive impedance-matching algorithms, and on-chip protection mechanisms
-
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
-
this PhD project, you will investigate the co-design between event-based learning algorithms and neuronal hardware units with multi-scale time constants. The algorithmic methodology will exploit recent
-
efficiency through parallelism (time-, frequency-, and mode-multiplexing), with a specific focus on photonic reservoir computing Relate parallelism to applications, e.g., algorithmic parallelism, multi-tasking
-
Relate parallelism to applications, e.g., algorithmic parallelism, multi-tasking, etc. Address nonlinear equalization in optical signal transmission and provide a comparison with neuromorphic electronics
-
on sufficient and sufficiently clean water. However, we often lack the data to fully understand the dynamics of contaminants throughout the urban water cycle. Existing sensors for water quality monitoring do not