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Field
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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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in GPU programming one or more parallel computing models, including SYCL, CUDA, HIP, or OpenMP Experience with scientific computing and software development on HPC systems Ability to conduct
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conditions. Implementing a multimodal approach for large-scale data analysis using CPU and GPU Solutions at the UM6P Data Center. Innovate and improve image analysis algorithms for plant trait quantification
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, with PyTorch and/or other GPU programming tools is also necessary. You should have completed all requirements for your PhD by the time you are hired. How to Apply: Candidates who have most, but not all
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), or reinforcement learning (RL) post-training. Experience with multi-GPU training and a strong working knowledge of reinforcement learning are also required. Familiarity with standard software development tools
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Preferred Qualifications: Experience in thermos-fluids in porous media. Experience in High-Performance Computing (HPC) on CPU or GPU platforms. Experience in mentoring of graduate and undergraduate students.
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experimental data. Experience in GPU programming. Job Family Postdoctoral Job Profile Postdoctoral Appointee Worker Type Long-Term (Fixed Term) Time Type Full time The expected hiring range for this position is
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with GPU-accelerated computation and high-dimensional data analysis. Enthusiasm for applying AI innovations to real biological and medical challenges. Required Application Materials: Cover letter
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provide a performance or efficiency advantage, and determine scenarios where conventional AI accelerators (such as embedded GPUs or FPGA-based accelerators) remain more appropriate due to data
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | 2 days ago
of data scientists/clinicians and working with unique datasets from multiple academic medical centers (e.g. UNC, UCSF, Mayo Clinic, Memorial Sloan Kettering, etc). Lab dedicated GPU workstations/servers and