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-funded fellow, you will have early access to the Empire AI clusters, utilizing state-of-the-art GPU architectures to push the boundaries of structural biology. This position is a prestigious Empire AI
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) environments, GPU-enabled systems, and cloud-based resources for large-scale data analysis and model development. Using these rich data resources, we develop and apply advanced AI/ML methods to model tumor
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massively parallel computing systems, including GPU-accelerated platforms. Develop and maintain scientific software, primarily in Python , using modern practices for software development, testing
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-based models for large dynamical systems (e.g. weather, hydrology). Expertise in data and model parallelisms for distributed training on large GPU-based machines is essential. Candidates with
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algorithms, pangenome graphs, or population genetics Deep learning for sequence, EHR, or imaging data High-performance and GPU computing environments Excellent candidates from adjacent quantitative
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group working on the LHCb experiment at the Large Hadron Collider. The successful candidate will focus on developing LHCb’s real-time data analysis systems, including the GPU -based software
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single-mode and multimode semiconductor laser arrays Optimization and acceleration of existing simulation codes Parallel computing and high-performance computing (HPC) GPU acceleration, where
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perturbations. This includes preprocessing large-scale single-cell and multi-omic datasets, defining model architectures, optimizing training pipelines on GPU clusters, and benchmarking against
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open source framework for adaptive mesh refinement, written in CUDA and supporting one-sided communication for scalability. The target architecture is a supercomputer with AMD MI300A GPU+CPU. 2
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perturbations. This includes preprocessing large-scale single-cell and multi-omic datasets, defining model architectures, optimizing training pipelines on GPU clusters, and benchmarking against existing