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-performance computing, GPU acceleration, automated benchmarking or large-scale numerical workflows. 4. Evidence of self-motivated contributing to collaborative research outputs, open-source software, preprints
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with leading machine learning frameworks and modern AI environments, including multi-GPU model training and large-scale inference on dozens to hundreds GPUs, are required. Additional Qualifications
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preprocessing large-scale single-cell and multi-omic datasets, defining model architectures, optimizing training pipelines on GPU clusters, and benchmarking against existing methods. Integrate and analyze large
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Python and experience with GPU cluster environments (e.g., SLURM) are a plus. Special Instructions Please provide a CV, a Research Statement, and two or more letters of recommendation. The target start
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system diagnostics. Experience with CUDA and deployment on NVIDIA edge-GPU platforms (Jetson Orin, Thor, or Spark). Experience calibrating and synchronizing LiDAR, IMU, camera, and wheel-odometry systems
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AI, …) and relevant programming frameworks Advanced programming skills in relevant programming languages and contexts (e.g., Python, HPC/GPU programming, big data applications) Strong team spirit and
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relevant programming frameworks Advanced programming skills in relevant programming languages and contexts (e.g., Python, HPC/GPU programming, big data applications) Strong team spirit and experience in
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computational resources (with GPUs) A collaborative environment across research fields, including plant biology, quantitative genetics, and population genetics Opportunities for collaboration and research visits
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networks, RNNs, LLMs) or in the deployment of such algorithms. Experience with specialized computational architectures such as GPUs, FPGAs, neuromorphic processors, or machine learning accelerators
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will be viewed highly favorably. Experience with GPU acceleration or code parallelization paradigms (e.g., MPI, OpenMP, CUDA). FLSA Exempt Full Time/Part Time Full Time Number of Hours Worked per Week 40