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transformation. Why Join Us? 🚀 Make an Impact: Play a key role in shaping financial strategies that influence the University’s growth and global standing. 📈Growth & Development: We believe in continuous learning
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healthcare system, comprising the largest physician network in Indiana, 16 hospitals across Indiana, and a deep academic partnership with the IU School of Medicine. Ideal Candidate Profile: An established NIH
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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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Initiatives in Forest Research (WIFORCE) program. The successful applicant will work on the development of bioacoustic monitoring methods using automated recording units (ARUs), deep learning methods, and
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collaborations in areas such as healthy living, transport, green energy, and advanced manufacturing. The role requires strong hands-on technical skills in modern AI, including deep learning, LLMs, Agentic AI, and
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success. Candidates should demonstrate (1) a mastery of their specific discipline; (2) a deep commitment to the college mission of fostering student success, achievement and persistence; (3) technology
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Engineering, Mechatronics, Computer Science, etc. Strong background in AI, Large Language Model, autonomous driving, deep learning, interaction modelling, prediction, robotics and automation. Candidates having
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) a deep commitment to the college mission of fostering student success, achievement and persistence; (3) technology competencies as well as teaching/counseling skills and strategies that facilitate
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(e.g. deep learning, language models and responsible AI). Minimum Qualifications PhD or other doctoral degree in biostatistics, statistics or strongly related field. Record of high-quality scientific
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and integration of multimodal neuroimaging, behavioral and clinical data, and building large-scale deep learning models for multimodal neuroimaging datasets to construct predictive network models in