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Health Prevention: Investigate how LLMs associated with deep learning can be used to identify early signs of mental health disorders by analyzing digital diaries, questionnaires, voices features, physical
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Classification Title: Assistant/Associate/Full Professor Classification Minimum Requirements: a Ph.D. in computer science or related field Expertise in AI/ML, deep learning Expert proficiency in
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Computer Engineering. Expertise in computer vision algorithms and image processing techniques (such as object detection, segmentation, and feature extraction). Proficiency in deep learning frameworks such as
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integration across identity management, research computing platforms, AI/ML workloads, and a growing portfolio of cloud-native and hybrid applications. This hands-on technical leadership role requires deep
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Specialized areas: Deep Learning, Generative AI, Prompt Engineering, Conversational AI and Chatbots, Reinforcement Learning Applied domains: Machine Learning for Cybersecurity, AI for 3D Imaging, Recommender
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of almost 11,000 individuals, including approximately 7,700 academic staff members, who passionately pursue answers to the profound questions that shape our future. Fueled by curiosity and a deep sense
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-driven organization Application instructions: Posting Number: 20220795-STAFF Posting Start Date: 03/16/2026 Application Deadline: 4/3/2026 To apply, visit https://stcatherine.peopleadmin.com/postings/10623
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, gender identity, or gender expression. To learn more about diversity at the U: http://diversity.umn.edu Employment Requirements Any offer of employment is contingent upon the successful completion of a
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at the intersection of mathematics and AI safety, with a focus on developing rigorous mathematical foundations for AI interpretability. Research directions include mean field theories of deep learning, data attribution
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, training, product/platform ownership, or digital transformation roles. Incumbent must have the ability to blend front-facing, executive level consulting skills with deep technical knowledge to identify