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multiple modalities to build robust AI systems is an advantage Interdisciplinary Applications: Leveraging LLM / VLMs for interdisciplinary problems, such as: AI-driven scientific discovery, automating
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clinical translation. Our work is supported by multiple NIH grants, including funding from NIAMS, the NIH AMP-AIM Team Science Leadership Scholars Program, and the Office of Research on Women’s Health, as
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documentation. Strong interpersonal, written and oral communication skills. Exceptional time management skills. Ability to effectively prioritize and manage multiple projects and deadlines. Ability to exercise
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in such a way as to be accessible by lay and professional audiences Technology • Using R, SAS, Python, SPSS, Tableau, and/or other database/analytical software platforms at an intermediate level of
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. Design, develop, and test business intelligence outputs (including visualization) to ensure useful and insightful information using software including R, Python, Tableau, and/or Excel to support research
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of experience beyond PhD, or MS with 4 years’ experience Technical proficiency with quantitative data analytics and workflows (e.g. R, Python, GIS, Modflow, GitHub) Experience with experimental design and complex
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mechanisms that support learning and memory. We use large-scale electrophysiology and imaging to record hundreds to thousands of neurons across multiple brain areas in behaving rodents. To test the role
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defined standards for data integration. Integrate data from source systems into a common data layer and integrate with multiple data formats Other duties as assigned. Qualifications Bachelor's degree in
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generate a new understanding of MoSS. These advances will have applications across multiple sectors, including pharmaceuticals, agrochemicals, and food industries. There are multiple opportunities
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proficiency in Python (e.g., NumPy, Pandas, scikit-learn, PyTorch, TensorFlow); additional experience with R, MATLAB, or Julia is an advantage. Machine Learning Expertise: Familiarity with supervised