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
- Eindhoven University of Technology (TU/e)
- European Space Agency
- University of Groningen
- University of Twente (UT)
- Eindhoven University of Technology (TU/e); Eindhoven
- Eindhoven University of Technology (TU/e); 27 Sep ’25 published
- University of Amsterdam (UvA)
- University of Amsterdam (UvA); 26 Sep ’25 published
- 1 more »
- « less
-
Field
-
energy generation, high-tech systems, electric vehicles, industrial automation and more. At EPE Group, we are building a strong research line on the reliability and resilience of power electronics systems
-
-efficient artificial intelligence (AI) applications. However, this new computing paradigm faces various design challenges in terms of design and technology challenges, application mapping and reliability
-
in terms of design and technology challenges, application mapping and reliability issues. Thus, there is a growing demand for efficient and reliable digital CIM-based neuromorphic system design which
-
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
-
. 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
-
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
-
the performance and durability of the new SCMs, ensuring their reliability for construction applications. The postdoc researcher will join the Building Materials Group which contains 25 PhDs, 3 postdocs, 2
-
. You will be part of the Mathematics of Imaging & AI chair , which has ample expertise in the development of reliable and robust deep learning methods with clinical impact. The project is highly
-
to measurements and benchmarking services for external partners. This includes performing characterization of sensor devices developed by collaborators, supporting QSTeM’s mission as a reliable and high-performance