Sort by
Refine Your Search
-
Employer
- Forschungszentrum Jülich
- DAAD
- Technical University of Munich
- Fraunhofer-Gesellschaft
- Heidelberg University
- Leibniz
- Max Planck Institute for Innovation and Competition, Munich
- Max Planck Institute for Radio Astronomy, Bonn
- Max Planck Institute of Geoanthropology, Jena
- NEC Laboratories Europe GmbH
- University of Tübingen
- 1 more »
- « less
-
Field
-
Your profile: Preferably a doctoral degree, but MSc are also encouraged to apply Expert knowledge in one or several of the following High Performance Computing GPU computing Array Computing with JAX A
-
benchmark them with a realistic case study. The main focus of the project can develop either more in the mathematical theory of MCMC, the implementation of code for the Jülich supercomputers (GPU/CPU
-
, including Large Language Models (LLMs), agent-based systems, and Retrieval-Augmented Generation (RAG). Practical expertise in training and optimizing neural networks on high-performance (GPU-enabled
-
, PyTorch) for ML applications, training, evaluation, and deployment of models Use of GPU-based servers and modern IT infrastructure for training and inference Application of classical ML methods (e.g
-
. 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), Horovod, or DeepSpeed, and in
-
Implement sustainable and reproducible and FAIR research software engineering practices Collaborate with other HPC facilities and project partners Help evaluate and integrate GPU acceleration and other modern
-
plasma physics (XGC, IPPL). Expected qualifications: A Master's degree in Computer Science or Applied Mathematics. Necessary knowledge: Modern C++, GPU computing with CUDA/SYCL, MPI, Krylov solvers
-
on the 1D or analytical model) Hybrid simulation approach (e.g., which combine CFD and 1D simulations) High Performance Computing and/or GPU programming for this domain Machine learning algorithms
-
of microfluidic devices. Simulation for microfluidics. (CFD) High Performance Computing and/or GPU programming for this domain. Machine learning algorithms for this domain Clean energy solutions (e.g., microfluidic