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Field
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establish a research profile. Develop and execute innovative research projects. Develop, train, and evaluate modern machine-learning models on GPU/HPC infrastructure. Integrate AI methods with scientific
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-learning architectures for sequential data (e.g., Transformers, graph neural networks, state-space models). Experience with OpenCV, GPU-accelerated inference, Docker, and modern software engineering
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Nextflow or Snakemake, version control such as Git, and reproducible computational environments is preferred. Familiarity with GPU-accelerated genomics, high-performance computing, or cloud-based analysis
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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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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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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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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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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
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, validation, calibration, and inference. Working with large, longitudinal, structured and unstructured datasets in Linux and high-performance or GPU-accelerated computing environments. Applying rigorous methods