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assigned Requirements A Ph.D. degree in Neuroimaging, Biomedical Engineering, Electrical Engineering, Physics, Computer Science, or other related disciplines, with experience or keen interest in processing
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of machine learning, neuroimaging and Psychiatry, developing methods with the potential to improve our ability to predict clinical outcomes. They will have the opportunity to work with rich brain MRI datasets
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intelligence, data science, medical physics, neuroimaging, bioengineering, or related disciplines, accompanied by accredited training in machine learning, deep learning, or medical image analysis. Experience: A
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well as machine learning and multivariate decoding of neuroimaging data to predict subjective experiences and individual differences. Successful candidates will be supported in building a research program at the
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field Demonstrated expertise in one or more of the following areas: Machine/deep learning, artificial intelligence, statistical modeling, or computational modeling Human neuroimaging analysis, including
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with neuroimaging and neural signal processing tools, including fMRI, structural MRI, diffusion MRI, EEG, or related modalities. Strong publication record in AI, machine learning, computational
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, deep reinforcement learning, and human-machine interaction. Facilitate strategic collaborations across departments within the College of Engineering and with the UM Miller School of Medicine, to advance
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. Teamwork: Ability to work collaboratively with others and contribute to a team environment. Technical Proficiency: Skilled in using office software, technology, and relevant computer applications
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analysis packages, basic shell scripting, experience in Unix/Linux platform) and experiences with deep learning tools (e.g., PyTorch, TensorFLow, Keras), neuroimaging analysis tools (e.g., PMOD, SPM, FSL
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practical knowledge of statistical and programming packages is mandatory, including R, Python, and/or SQL. Extensive experience with advanced statistical techniques and algorithms, such as Machine Learning