Sort by
Refine Your Search
-
Listed
-
Category
-
Employer
- Delft University of Technology (TU Delft)
- Delft University of Technology (TU Delft); 16 Oct ’25 published
- Delft University of Technology (TU Delft); Published yesterday
- Radboud University
- University of Amsterdam (UvA)
- University of Amsterdam (UvA); Published today
- Vrije Universiteit Amsterdam
- Vrije Universiteit Amsterdam (VU)
-
Field
-
strategies for programming, modeling, and integrating reconfigurable/spatial architectures, such as FPGAs and ML accelerators, within heterogeneous ICT ecosystems.Reconfigurable and Spatial hardware, such as
-
). Strong academic background and competencies in parallel programming, distributed computing, and performance engineering. Familiarity with accelerator programming (e.g, GPU), hardware programming, high
-
learning, deep learning, and/or computer vision; Experience in programming. Python is a must, lower-level GPU programming experience is a bonus; Strong grasp on the English language; Eager to collaborate and
-
background in machine learning, deep learning, and/or computer vision; Experience in programming. Python is a must, lower-level GPU programming experience is a bonus; Strong grasp on the English language
-
in high-performance computing using MPI. Experience in GPU programming using OpenACC, CUDA, CUDA-Fortran, Julia, or related tools. Experience in CFD meshing software. TU Delft (Delft University
-
recommended. Other valuable skills include: Experience in high-performance computing using MPI. Experience in GPU programming using OpenACC, CUDA, CUDA-Fortran, Julia, or related tools. Experience in CFD
-
6G testbeds (indoor and outdoor) with GPU clusters and edge computing platforms Global Internet measurement infrastructure and satellite network access Opportunities to engage with Internet
-
, which has multiple test machines with GPUs and AI accelerators. The algorithms used can be bound by the available compute power or memory bandwidth in different parts of the program. This information will