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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 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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, 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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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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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 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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/Python coding, next-generation sequencing data interpretation, large-scale data integration, and machine learning. Science: strengthen the ability to formulate hypotheses, design aims to test the
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, biologics, and cannabis. Apply statistical and machine learning approaches (e.g., sequence analysis, latent class analysis, clustering) to examine medication use trajectories and patient subgroups
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. • Develop computational and theoretical models that bridge neural data and behaviour, leveraging modern machine‑learning toolkits. • Drive multi‑lab collaborations across SCENE; co‑author high‑impact
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clinical research. Required Qualifications: A PhD in computational biology, bioinformatics, genetics, AI, machine learning, computer science, or a related field. Demonstrated experience analyzing single-cell