Sort by
Refine Your Search
-
Listed
-
Category
-
Employer
- Delft University of Technology (TU Delft); Delft
- Delft University of Technology (TU Delft)
- University of Twente
- University of Groningen
- University of Twente (UT)
- Eindhoven University of Technology (TU/e)
- Eindhoven University of Technology (TU/e); Eindhoven
- Erasmus MC (University Medical Center Rotterdam)
- KNAW
- Maastricht University (UM); Maastricht
- University of Twente (UT); Enschede
- 1 more »
- « less
-
Field
-
human robot interaction (e.g., sensory adaptive, child-led, reciprocal AI) Develop LLM-driven dialogue systems integrated into robotic and/or digital platforms Prototype and evaluate multimodal interfaces
-
. This will include a detailed assessment of existing methods to address such risks, and on how to achieve a better use and exchange of existing protocols and data. The project includes the use of Digital Twins
-
existing methods to address such risks, and on how to achieve a better use and exchange of existing protocols and data. The project includes the use of Digital Twins to simulate cascading disaster effects
-
Join us to create new smart molecules for imaging of complex mechanics. Job description Soft matter such as polymers and hydrogels are incredibly versatile materials. Their use in metamaterials and
-
such risks, and on how to achieve a better use and exchange of existing protocols and data. The project includes the use of Digital Twins to simulate cascading disaster effects, as well as satellite and sensor
-
knowledge or hands-on experience with Python, ImageJ, and/or MATLAB is highly beneficial for image processing and data analysis. Communication: Excellent written and spoken communication skills in English are
-
Unravel the complexity of valve disease in heart failure using Digital Twin technology. Help transform how cardiologists decide when and how to treat patients through personalized computer
-
for realizing the physical learning paradigm developed at AMOLF. You will integrate these memristive devices with the reconfigurable nonlinear processing units (RNPUs) developed in the NE partner group to harness
-
, and multiphysics coupling, which are difficult to capture in-situ with traditional methods. Typically, imaging is possible in such application, but full mechanical characterization is not. Our vision is
-
lengthy processing times associated with sequencing. This PhD project aims to develop innovative artificial intelligence (AI) methodologies by integrating histopathology images and RNA sequencing data