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to apply. Experience with data handling softwares (e.g., R, SAS, Python, Unix Shell) and genetic softwares (e.g., DMU, F90, AIREML). Background in working with Livestock data is an advantage. Can speak and
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field that provides a strong research background for the project. Fluency in English and Python are required. Research experience working with large-scale machine learning projects, extensive research
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-based proteomics data (DDA/DIA) Familiarity with software frameworks such as DIA-NN, FragPipe, or comparable platforms Strong programming skills in Python and/or R, and solid understanding of statistical
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publications Strong programming skills in Python and deep learning frameworks such as PyTorch, demonstrated by coursework, projects, or contributions to public code repositories Experience with or strong
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field that provides a strong research background for the project. Fluency in English and Python are required. Research experience working with large-scale machine learning projects, extensive research