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Field
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Associate Research Scientist / Post-Doctoral Associate in the Division of Science (Computer Science)
include: Strong foundation in one of the following areas: Machine Learning / Information Retrieval / Knowledge Graph Representation / Recommender Systems Graph Theory/Network Science Python, and up-to-date
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contributions Demonstrated experience implementing, training, evaluating, or fine-tuning modern machine learning models Strong programming skills in Python and experience building and maintaining research code
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willing to receive assistance and coaching when needed from the faculty lead. Prior experience in a diverse higher education institution preferred. Knowledge of Python and/or R programming languages is
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, Python, R, etc.) A relevant certification and/or industry experience are preferred. Other Requirements Ability to travel and work beyond normal office hours, if needed. Ability to maintain confidentiality
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of Microcomputers, Introduction to Computer Information Systems, Introductory Programming in Python, and Data Analytics Courses. Required Qualifications Candidates must hold a minimum of a Master’s degree in the
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demonstrated proficiency in programming, specifically in Python and R, as well as experience with modern deep learning frameworks like PyTorch or TensorFlow. In addition to technical skills, the candidate must
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Learning, or related fields, completed by the start date. Experience A strong foundation in core AI methodologies—machine learning, deep learning, NLP—coupled with hands-on expertise in Python, PyTorch, and
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programming skills in Python; initial experience with machine learning frameworks such as PyTorch or TensorFlow Initial practical experience from a master's thesis, study projects, internships, or open-source
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required. Substantial experience in machine learning, Python and R programming, and familiarity with deep learning packages (e.g., TensorFlow, Keras, or PyTorch) are essential. Additional Qualifications
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& Advanced Analytics Build and maintain reproducible workflows using tools such as Python, R, Bash, Docker/Singularity, Git, and workflow engines (e.g., Snakemake, Nextflow). Integrate established neuroimaging