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Field
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Science, Computer Science, Applied Mathematics and Statistics, Electrical and Computer Engineering, Biomedical Engineering, or a related field. Experience with a deep learning framework like PyTorch. Strong
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, Mixture-of-Experts; distributed training/inference (e.g. FSDP, DeepSpeed, Megatron-LM, tensor/sequence parallelism); scalable evaluation pipelines for reasoning and agents. Federated & Collaborative
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-driven experiment design. Optimize pipelines for performance, parallelization, and near real-time operation during beam time. Contribute to simulation tools to test imaging concepts, predict performance
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design. Optimize pipelines for performance, parallelization, and near real-time operation during beam time. Contribute to simulation tools to test imaging concepts, predict performance, and support
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parallel computing techniques including working in the cloud. Preferred Qualifications Education: No additional education beyond what is stated in the Required Qualifications section. Certifications
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and risk assessment modeling. • Prior experience with CCUS research projects. • Familiarity with high-performance computing (HPC) and parallel computing techniques. Required Knowledge, Skills, and/or
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professional responsibilities of the specialty occupation position. This requirement is common to this industry in parallel positions among similar organizations. The annual base salary range for this position
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Area of research: Scientific / postdoctoral posts Job description: GFZ is Germany's national centre for solid Earth research. We advance the understanding of dynamic processes to address global
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platform enables us to test hundreds of different conditions in parallel and assess their impacts on human immune responses, such as antibody production. We routinely work with industry partners to exploit
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leading peer-reviewed journals and conferences. Researching and developing parallel/scalable uncertainty visualization algorithms using HPC resources. Collaboration with domain scientists for demonstration