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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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and aggression, using optogenetics, in vivo imaging, electrophysiology, and sophisticated machine learning/artificial intelligence analyses of mouse behavior. All projects have translational components
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from varied sources, and machine learning methodologies. The underlying data are complex and will require sophisticated data management and integration skills. A candidate should have proficiency with
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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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Appointment Term: Initially 1 year, renewable Appointment Start Date: Fall 2025 but flexible Group or Departmental Website: https://www.liwanglab.org/ (link is external) How to Submit Application Materials
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computer skills and ability to quickly learn and master computer programs. Ability to work under deadlines with general guidance. Excellent organizational skills and demonstrated ability to complete detailed
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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
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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 programming background
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nano-mechanics, and machine learning as it applies to the field of computational mechanics. Candidates will be given opportunities to develop their teaching experience by designing and teaching a class
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infections using electronic phenotyping, supervised machine learning, live Epic/FHIR implementations for silent deployment, and multi-site data coordination. https://reporter.nih.gov/search