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Field
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-performance computing facility managed by the University with a general-purpose cluster consisting of 16,616 CPU cores and approximately 100 GPUs, as well as a dedicated AI cluster with 40 A100/H100 GPUs
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, Git, Azure Data Studio, pandas, NumPy, and Scikit-learn. Experience with High-Performance Computing (HPC) platforms with advanced skills in SLURM-based multi-node, multi-GPU training, data parallelism
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. Prior work with databases used for organizing large-scale processing. Prior use of and/or software development with GPUs. Experience with visualization of large data sets. An understanding of how to make
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and H100 GPUs, combined with pre-processed large-scale biobank data such as UK Biobank and ADSP, enabling you to work at the scale required for breakthrough research. The role offers exceptional
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environments, cloud computing, or GPU-accelerated machine learning Background in Monte Carlo Tree Search (MCTS) or reinforcement learning for sequence generation Familiarity with biological sequence alignment
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, Hospital, Translational Medicine Research Center (KUTTAM), and Koç University Is Bank Artificial Intelligence Research Center (KUIS AI). Koç University is home to Türkiye’s largest GPU cluster, providing
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mathematics and engineering. The Interpretable Machine Learning Lab has dedicated access to high-performance CPU and GPU computing resources provided by Duke University’s Research Computing unit and state
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models (e.g., CNNs, diffusion models, etc) Proficiency in Python Experience with HPC (CPU or GPU, with GPUs preferred) Related Skills and Other Requirements Ability to collaborate on the application of AI
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communicate effectively with the team to debug the model. The OLG model codebase is technically challenging and computationally demanding. For example, it solves dynamic programming problems with GPU
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library research and field explorations. Run experiments, analyze results, and prepare research outputs. Execute large-scale training jobs on GPU clusters, track metrics, visualize findings, and contribute