Sort by
Refine Your Search
-
Listed
-
Country
-
Employer
- DAAD
- Forschungszentrum Jülich
- Universite de Moncton
- CNRS
- ; University of Southampton
- Curtin University
- Delft University of Technology (TU Delft); 16 Oct ’25 published
- Erasmus MC (University Medical Center Rotterdam); Rotterdam
- European Magnetism Association EMA
- Ghent University
- Instituto Superior Técnico
- Instituto de Telecomunicações
- Karlsruher Institut für Technologie (KIT)
- Medizinische Universitaet Wien
- Nature Careers
- Radboud University
- Reykjavik University
- Technical University of Denmark
- Technical University of Munich
- The University of Manchester;
- University College Dublin
- University of Birmingham;
- University of Southern Denmark
- VU Amsterdam
- 14 more »
- « less
-
Field
-
resources, including 200+ NVIDIA A100 GPUs and group workstations. Image quality will be assessed using quantitative metrics and clinical expert qualitative review. Privacy safeguards will be built
-
suitable for part-time employment. Starting date: 17.10.2025 Job description: Design, develop and apply an flexible and integrative multiscale FWI using GPU-accelerated spectral-element simulations (Salvus
-
, and supportive atmosphere, equipped with state-of-the-art research facilities, including dedicated GPU clusters, data servers, and personal GPU-enabled workstations. You will join a multidisciplinary
-
/GPUs. These devices provide massive spatial parallelism and are well-suited for dataflow programming paradigms. However, optimizing and porting code efficiently to these architectures remains a key
-
secretion systems (T4SS). The laboratory provides all the equipment required for the project, including standard microbiology facilities (L1 and L2) and biochemistry equipment (AKTA pure), GPU computing
-
in rivers or estuaries with applications to plastic transport. The transport models will be included in existing shallow-water solvers running in hybrid CPU-GPU architectures. The expected workplan
-
, as is experience with scientific, numerical, and/or GPU programming; • Have some prior experience with data science and/or machine learning; • Hold values such as honesty, modesty, collectivism
-
optimisation or machine learning (e.g., Python/Matlab/C++; PyTorch/TensorFlow). Experience in signal processing/wireless or SDR/GPU prototyping is a plus. Demonstrated research potential is highly desirable
-
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
-
technological platforms on site. The PhD student's work will be carried out at the IGBMC's integrative biology center. He/she will have privileged access to the team's computing server (GPU node) and the