Sort by
Refine Your Search
-
Listed
-
Employer
- Delft University of Technology (TU Delft)
- Delft University of Technology (TU Delft); yesterday published
- Delft University of Technology (TU Delft); Delft
- University of Amsterdam (UvA)
- University of Twente
- University of Twente (UT)
- Delft University of Technology (TU Delft); Published yesterday
- Eindhoven University of Technology (TU/e)
- Eindhoven University of Technology (TU/e); 27 Sep ’25 published
- Leiden University
- University of Amsterdam (UvA); 26 Sep ’25 published
- University of Amsterdam (UvA); Published today
- University of Groningen
- University of Twente (UT); Published today
- 4 more »
- « less
-
Field
-
structures and one of the most iconic engineering achievements of the Dutch Delta Works. Ensuring its continued reliability under changing climate is both a national priority and an exciting scientific
-
. However, these new technologies and computing paradigms face various design challenges in terms of design and technology challenges, application mapping and reliability and non-ideality issues. Thus
-
, application mapping and reliability and non-ideality issues. Thus, there is a growing demand for efficient and reliable memristor CIM-based neuromorphic system design which includes techniques such as
-
trustworthiness and reliability are critical. In this project, you will explore neuro-symbolic methods that integrate LLMs (or Generative AI more broadly) with Symbolic AI techniques. In this hybrid approach
-
Advancing hydrogen aviation: optimize compressor design of the air supply system to boost fuel cell poweetrain efficiency and reliability. Job description To reduce greenhouse gas emissions in
-
) mining applications to study the best options for robust, reliable deep-sea mining systems, and how to encourage industry to use and develop equipment with the least negative impacts for potential future
-
) mining applications to study the best options for robust, reliable deep-sea mining systems, and how to encourage industry to use and develop equipment with the least negative impacts for potential future
-
are challenging to detect early with conventional single-sensor approaches. To ensure reliability and enable predictive maintenance, there is a pressing need for AI-supported, high-speed non-destructive monitoring
-
interpretable, reliable, and scalable ML methods, neural quantum states, understanding the simplicity bias of overparameterized neural networks, or applying them to quantum systems, such as ultracold quantum
-
they do not eliminate them entirely. This poses a challenge in applications where trustworthiness and reliability are critical. In this project, you will explore neuro-symbolic methods that integrate LLMs