Sort by
Refine Your Search
-
Listed
-
Category
-
Country
-
Program
-
Field
-
with in-depth knowledge of parallel programming (GPU, multi-threading, etc.). - Familiarity with standard collaborative development tools: Git, GitHub, CMake, Guix-HPC, Spack, GTest, CTest, etc
-
) Established Researcher (R3) Application Deadline 30 May 2026 - 22:00 (UTC) Country France Type of Contract Temporary Job Status Full-time Is the job funded through the EU Research Framework Programme? Not
-
languages; experience with GPU programming (e.g., CUDA) is highly desirable. Background in optimization, image-guided radiotherapy, medical imaging, or computational modeling. Experience with treatment
-
phlebotomy. Additionally, the GPU is home to the GI Division's Motility program offering short and long motility studies, Bravo, Impedance Probes and EndoFlip diagnostic tests. The GPU is also home to
-
physics, mathematics or any related field. What we offer State of the art on-site high performance/GPU compute facilities Competitive research in an inspiring, world-class environment A wide range of offers
-
at the interface of computational systems biology and mathematics/statistics with a strong attitude to open research software development. For more information visit http://www.fz-juelich.de/ibg/ibg-1/modsim
-
data from the European XFEL facility at DESY. Project website: https://www.mpinat.mpg.de/628848/SM-Ultrafast-XRay-Diffraction Your profile Eligible candidates have strong skills in computational
-
data from the European XFEL facility at DESY. Project website: https://www.mpinat.mpg.de/628848/SM-Ultrafast-XRay-Diffraction Your profile Eligible candidates have strong skills in computational physics
-
commonly used on Unix systems. Additional languages or experience with libraries for utilizing GPU hardware efficiently, e.g., CUDA, are a plus. Experience in AI programming with, e.g., PyTorch(-DDP
-
members in designing and integrating solutions into the AI(X) compute, software and data infrastructure stack, hardening these solutions, testing these on modern high-performance GPU compute clusters, and