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, distributed control, formation control, perception, and decision-making in complex environments. Design and implement data-driven approaches, including machine learning, reinforcement learning, surrogate models
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reinforcement learning approaches for autonomous guidewire shaping and control. • Design experiments, validation strategies, and quantitative performance metrics for robotic navigation and wire-shaping systems
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that can be repurposed for viruses with pandemic potential. This includes working closely with computer scientists to utilize published “omics” datasets and machine learning approaches to identify FDA
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publications prior to the panel interview. In addition to further excelling your skills in Computer Vision/Big Data/Machine Learning analyses, this opportunity enables you to: - Work closely with clinicians
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Current Employees: If you are a current Staff, Faculty or Temporary employee at the University of Miami, please click here to log in to Workday to use the internal application process. To learn how
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curious to deliver work that matters, your journey starts here! The Machine Learning Department (MLD) at Carnegie Mellon University is a leading hub for research and education in artificial intelligence and
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; • Familiarity with statistical analyses and modeling as well as machine learning approaches, supported with strong skills in computational optimization of methods; • Experience working with large-scale datasets
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centralized. The successful candidates will work at the intersection of federated learning, foundation models, multimodal biomedical AI, privacy-preserving machine learning, continuous learning, and agentic AI
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Postdoctoral Researcher in artificial intelligence and machine learning (AI/ML) for advanced tuning and diagnosis of particle accelerators. The Accelerator Operations and Physics (AOP) Group
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(AI) and machine learning (ML) methodologies. The position involves annotating clinical data, collaborating with AI/ML/NLP teams, developing algorithms, and generating insights to improve patient care