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Field
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for outcome assessments for a range of brain diseases. The individual will schedule, train, and supervise research assistants, students, and instructors. They must ensure: 1) patient confidentiality and safety
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collaboratively within the CCIA team and coordinate with CCIA stakeholders and campus systems. Preferred Qualifications Experience in developing and deploying web applications that are accessible across multiple
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automated recovery algorithms, improving system resilience. Research Areas for Master’s and PhD Students AI-Enhanced Resource Forecasting and Optimization: Research Focus: Developing and testing ML algorithms
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University of California San Francisco | San Francisco, California | United States | about 1 month ago
to develop and implement pulse sequences and reconstruction algorithms on Siemens and GE platforms. Assisting in protocol optimization to ensure consistency and quality across imaging studies. Providing
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management systems in complex organizations with multiple stakeholders, authors, and editors. Proven experience in marketing, with a focus on strategic marketing leadership and management within the digital
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. Provide accurate and timely follow-up with staff, patients and families. Process accurate and timely admissions as requested. Patient placement in accordance with placement guidelines, fill algorithms and
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data into the software database. Aid in the development of processes, applications, and reports to enhance the efficiency of delivering and integrating Performance Science data into the database
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About This Role The Resource Demand Forecasting Lead will be responsible for managing and forecasting resource needs across multiple functions in our Quantitative Sciences and Development Operations
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validate artificial intelligence (AI) algorithms for analyzing OCT imaging in diabetic macular edema (DME). Data processing and image annotation to support development of AI models trained on OCT scans from
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motivated the development of Federated Learning (FL) [1,2], a framework for on-device collaborative training of machine learning models. FL algorithms like FedAvg [3] allow clients to train a common global