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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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the ability to work on multiple projects both independently and simultaneously to meet set deadlines. If needed, works outside standard work hours to meet project deadlines or to implement moves to production
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, the ability to establish priorities and coordinate multiple tasks, and to work independently. ▪ Ability to work effectively as a team member on projects with a diverse group of research staff. ▪ Ability
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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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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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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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. 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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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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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
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in programming languages such as Python and C++. Goal is the development and integration of new and automated measurement capabilities for ultra-sensitive low-energy AMS. This includes extending and