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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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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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ABILITIES (REQUIRED): Demonstrated success in leading teams to apply data science, machine learning, and other advanced computing techniques to solve complex research problems. Expertise in data science
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, statistics, or mathematics OR a strong background in gene engineering and functional interrogation of hematopoietic stem and progenitor cells. Strong knowledge in bioinformatics, machine learning, statistics
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specimens for microscopy essential but could be taught. Additionally, assistance to the PI (MD) and a research scientist (Phd) in grantwriting and manuscript writing may be asked. QUALIFICATIONS: Minimum 2
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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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, experience with databases and scientific applications, and ability to quickly learn and master computer programs. • Strong analytical skills and excellent judgment. • Ability to work under
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, including experience with R or Python Experience with multi-omics data analysis and/or experience with brain aging research Strong understanding of statistical methods and machine learning techniques
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outcome to transcranial magnetic stimulation (TMS) in depressed patients. Using machine learning, our projects seek to maximize this EEG biomarker and improve the efficacy of this treatment. The candidate
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. General computer skills and ability to quickly learn and master computer programs, databases, and scientific applications. Ability to work under deadlines with general guidance. Excellent organizational