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programming (Python) and Linux skills is a must Knowledge in plant biology or single-cell genomics is a plus You have good communication skills, are a team player, and are proficient in English We offer a 3
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analysis and handling omics' datasets - genomics transcriptomics, and proteomics Proficiency in R, Python, and other programming languages Expertise in Linux, Git, Docker, and other high-performance
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' datasets - genomics transcriptomics, and proteomics Proficiency in R, Python, and other programming languages Expertise in Linux, Git, Docker, and other high-performance computing environments Excellent
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advanced expertise in developing AI applications using offline/online LLMs (e.g., GPT, Qwen, DeepSeek, Mistral, Llama, Gemma) within Linux environments. They must also demonstrate proficiency in clinical
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, or computational biology Proficiency in Python and experience working in Linux-based HPC environments or cloud computing platforms Proven experience with deep learning frameworks such as PyTorch or TensorFlow, and
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. Expertise with R and Linux shell is essential. Python experience preferred. Does this position have supervisory responsibilities? No Preferred Education/Experience Statistical genetics, genetic epidemiology
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/Python coding experience with proficiency in RStudio, RMarkdown, and common Bioconductor packages. Computing (Advanced): Experience with Linux servers and computing cluster. Communication (Proficient
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), familiarity with Linux Proficiency in English and ability to work in a team Outstanding analytical and problem-solving skills Employment terms: The successful candidate will be employed by Mohammed VI
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | 2 days ago
Python and Mathematica, use of linux, git, and latex. Special Physical/Mental Requirements Special Instructions For information on UNC Postdoctoral Benefits and Services click here Quick Link https
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working in Linux and/or high-performance cluster environments. A strong ability to perform analytical reasoning to extract biological insights from data-driven approaches will be critical. In addition