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university located in the heart of Detroit, Michigan where students from all backgrounds are offered a rich, high-quality education. Our deep-rooted commitment to excellence, collaboration, integrity
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. Apply deep learning approaches to support optimization of segmentation methods for clinical neuroimaging datasets. Investigate developmental differences in infant brain functional networks. Support
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studies. Strong background in AI/ML fundamentals and extensive experience with deep learning (DL) methods. Demonstrated proficiency in Python and machine learning frameworks (e.g., PyTorch, Jax, scikit
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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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integrates neuroimaging, sleep measurement, digital phenotyping, electronic health record (EHR) data, and deep clinical phenotyping to identify predictors of symptom trajectories and functional outcomes in
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, with a focus on building multimodal AI models to predict dental caries progression. The successful candidate will work on developing deep learning and computer vision models using longitudinal dental
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experience with deep learning (DL) methods. Demonstrated proficiency in Python and machine learning frameworks (e.g., PyTorch, Jax, scikit-learn) applied to genomic/related datasets. Experience with sequence
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deep learning including data collection, architecture development, model training, and validation Interest in software development, with particular emphasis on the Python programming language and
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Postdoctoral Associate - AI3 Required Qualifications: (as evidenced by an attached resume) PhD (or foreign equivalent) in hand by the start of the appointment. Preferred Qualification: A
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methods, risk and reliability, stochastic control processes, dynamic programing, deep reinforcement learning. Strong track record in scientific contributions supported by peer-reviewed publications. Strong