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willingness to learn: High-performance computing (distributed systems, profiling, performance optimization), Training large AI models (PyTorch/JAX/TensorFlow, parallelization, mixed precision), Data analysis
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the scientific landscape of the institute. You will be part of an innovative project in the field of High Performance Computing, with an emphasis on scientific work rather than client project work. All five team
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multiple countries. Your profile: PhD in Bioinformatics, Computational Biology, Statistics, Computer Science, or related field Experience with high-throughput genomic data analysis Experience in machine
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Helmholtz-Zentrum Dresden-Rossendorf - HZDR - Helmholtz Association | Gorlitz, Sachsen | Germany | 4 days ago
programming skills in languages such as Python, C/C++ and CUDA # Familiarity with modern deep learning frameworks like Tensorflow 2.x.x, PyTorch # Mandatory experience with High-Performance Computing (HPC
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Center for Advanced Systems Understanding, Helmholtz Center Dresden-Rossendorf | Germany | 6 days ago
environment Excellent programming skills in languages such as Python, C/C++ and CUDA Familiarity with modern deep learning frameworks like Tensorflow 2.x.x, PyTorch Mandatory experience with High-Performance
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English skills High level of motivation for independent and team-oriented research work Proficient computer skills, particularly in MS Office (Excel, Word) Flexibility, commitment, team spirit, and a strong
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Helmholtz-Zentrum Dresden-Rossendorf - HZDR - Helmholtz Association | Dresden, Sachsen | Germany | 4 days ago
Programme? Not funded by a EU programme Is the Job related to staff position within a Research Infrastructure? No Offer Description Area of research: Scientific / postdoctoral posts Starting date: 01.04.2026
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. For computational analyses, the successful candidate will have access to the high-performance computing infrastructure of the Leibniz Supercomputing Centre (LRZ) and dedicated data management support. We offer close
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, biophysics, epi /genetics, (bio)informatics, and multimodal data analysis. In this project, we will elucidate the molecular mechanisms that enable sperm to sense their chemical and physical environment, with a
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occurrences along gradients of anthropogenic stress. Additionally, dispersal and connectivity information will be included to enhance SDM performance. Our aim is to integrate abiotic and biotic factors