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Field
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learning, and data science, with a particular focus on neuroscience applications. Designs AI techniques and algorithms for multimodal data fusion (e.g., MRI, EEG, cognitive and behavioral data, blood
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from sensors or other continuous data sources. Knowledge of a variety of machine learning techniques (clustering, decision tree learning, artificial neural networks, etc.) and their real-world advantages
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algorithms, clinical decision support systems, and population health management platforms. Evaluate emerging technologies in clinical informatics and provide strategic recommendations for their adoption within
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Experience: Knowledge of AI frameworks and algorithms, particularly those related to decision-making and ethical AI. Machine Learning Experience: Knowledge of ML techniques, including reinforcement learning
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formats. Experience with AI and deep learning algorithm development for medical image analysis. Familiarity with SQL, Python, Tensorflow, Scikit-Learn, and Pandas. Additional Qualifications Considered
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use of data and algorithms. Excellent written and verbal communication skills and ability to communicate effectively with a variety of different stakeholders, e.g., academics, business executives
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analyses in nonclinical drug development. The postdoctoral role involves designing and implementing algorithms for anomaly detection, segmentation, and classification to contribute to the development
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-centered design, and the health professions. Key areas of research include medication safety; mobile health, sensors, and other technologies; aging and family caregiving; chronic disease care; lifestyle
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to implement and optimize AI/ML models for biomedical datasets. Preferred Knowledge, Skills and Abilities Mathematical Modeling: Strong foundation in numerical modeling, graph theory, and statistics. Algorithm
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) the physics of morphogenesis, (ii) collective behavior of organisms across scales, (iii) soft matter physics, (iv) quantify resource consumption in a large residential community, using sensor deployment