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HPC concepts, including parallel computing, distributed systems, and optimization. Analytical skills, problem-solving abilities, and a growth mindset. Additional Qualifications Applicants should be
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-using models, retrieval systems, or related AI systems Experience with large-scale datasets, distributed training, or high-performance computing environments Interest in scientific applications of AI
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distributed computing environments. Knowledge of optimizing pipelines for large-scale genomic projects. Special Instructions Required documents: CV Research summary of PhD work. Cover letter
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in Linux/Unix-based high-performance computing ( HPC ) environments and job schedulers (e.g., SLURM or qsub); experience with cloud computing platforms (e.g., AWS , GCP , Terra) is a plus
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modeling brain circuits and neural activity from large-scale, multi-regional recordings Experience with large-scale datasets, distributed training, or high-performance computing environments Expertise in
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-training, adaptation, or evaluation Experience with agentic workflows, tool use, retrieval systems, or related AI systems Experience with large-scale datasets, distributed training, or high-performance
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cellular biology, including AlphaFold, RFDiffusion, CellCap, or related systems Experience with large-scale datasets, distributed training, or high-performance computing environments Expertise in
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modeling brain circuits and neural activity from large-scale, multi-regional recordings. Experience with large-scale datasets, distributed training, or high-performance computing environments. Experience
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, RFDiffusion, CellCap, or related systems. Experience with large-scale datasets, distributed training, or high-performance computing environments. Expertise in scientific applications of AI/ML in protein