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join the group to develop AI and machine learning based software to assist clinical workflow and pre-clinical studies. Required Qualifications: Ph.D. in a physical science or engineering field Strong
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. Responsibilities may include: Designing and conducting studies on the clinical impact of GLP-1 and other metabolic therapies Developing and applying computer vision and machine learning techniques to analyze
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subsea digital twin of deep-water mooring lines for floating offshore wind turbines. The digital twin will be integrated with machine learning algorithms for detection of primary entanglement due
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patients requiring urgent or emergent intervention. The fellowship provides comprehensive training in data engineering, exploratory analysis, statistical modeling, machine learning, and artificial
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, machine learning, statistics and programming skills (R and Python) is preferred. Record of peer-reviewed publications. Knowledge in one or more of the following areas is desirable: single-cell profiling
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scientists, and machine learning experts will be an essential and enriching component of the position. Strong candidates will have a background in machine learning and natural language processing (NLP), with a
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to significantly extend our existing team’s capabilities for data scoring and analysis (e.g., with expertise in natural language processing, machine learning, or computational modeling). Finally, the
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and early-onset cases without a known genetic cause. We are also interested in genetic interactions (epistasis), tandem repeats, machine learning, and other areas of AD research that have not yet been
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connectivity and graph-theoretic analyses Familiarity with MR sequence programming (Siemens or GE platforms) Machine learning / AI applied to neuroimaging data EEG acquisition and analysis Use of neuroanatomical
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one or more of the following areas is a BIG PLUS: data science (machine learning and AI), cancer biology, animal physiology, organic chemistry, E3-ubiquitin biology, and gene editing. In all cases