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. Modeling dynamical systems Designing and extending algorithms grounded in probabilistic machine learning Applying statistical techniques to assess robustness and generalization. Development of methods
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Genomics at Harvard Medical School Several positions are available in the Park Lab (https://compbio.hms.harvard.edu/ ). The aim of the laboratory is to develop and apply innovative computational methods
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postdoctoral fellows to perform cutting-edge research in AI for radiation therapy. Research areas include developing and implementing AI techniques for image-guided radiation therapy, such as image
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hospitals. PRIMARY DUTIES AND RESPONSIBILITIES: The qualified candidate will focus on developing new algorithms, including agentic artificial intelligence approaches, for the clinical integration
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algorithms, and non-inferiority margin derivation methodologies. You will develop proficiency in regulatory database management, systematic data extraction from NDA submissions, and implementation of network
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randomized evaluations of AI-driven interventions in housing, education, and workforce development, and building evaluation infrastructure for AI deployments in social sector organizations. The researcher
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, developing scalable algorithms for system optimization and control, conducting policy-relevant economic analysis, programming following best practices for reproducible research, presenting findings to academic
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Center for Devices and Radiological Health (CDRH) | Southern Md Facility, Maryland | United States | 16 days ago
on the development of optical models, image acquisition software, image processing algorithms, and deep learning algorithms using Zemax, Python, Matlab, LabVIEW, CUDA, and other programming environments. Training
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Hope National Medical center under Dr. Supriyo Bhattacharya. Our mission is to advance the next generation of in-silico algorithms for understanding diabetes and related disease mechanisms and
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challenge meeting this requirement is the simultaneous need for low-power consumption. The main objective of the project is to develop a complete end-to-end high-performance DNN system for on-premise