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Field
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). Practical experience with cloud computing platforms (e.g., AWS, GCP, Azure). Additional Qualifications Experience with multi-GPU model training and large-scale inference. Familiarity with modern AI
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Process) Certifications/Licenses Required Knowledge, Skills, and Abilities Experience in GPU programming Experience working in interdisciplinary research teams Experience working with large and complex
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large GPU clusters on cryoSTEM datasets in the multi-terabyte range. This position plays a pivotal role in supporting ongoing, high-impact research programs within our lab. The successful candidate will
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foundation models, directly advancing the frontier of computational biology and machine learning. You will also implement parallel systems capable of training such models across large GPU clusters on cryoSTEM
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or High-Performance Computing clusters for GPU-intensive tasks. Desired: Masters degree in computer science, Data Science, Digital Humanities, or a related field. The College of the Arts and Sciences
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substantial investments in on-premises GPU infrastructure that allows us to work with confidential partner data under full data control. The focus of the position is on research, aiming at publications in
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. Experience with high-performance computing, cloud computing, GPU acceleration, or distributed data processing. Experience participating in international scientific collaborations and/or multi-institutional
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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
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population and comparative genomics to examine genetic diversity, selection, pangenome relationships, and functional conservation. You will also develop reproducible GPU- and CPU-based high-performance
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software (e.g., Paraview). Preferred Qualifications: Exposure to developing agentic workflows. Code development using Git repositories, GPU computing. Development of agentic workflows for scientific