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Field
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learning-based computer vision algorithms and software for object detection, classification, and segmentation. Key Responsibilities Participate in and manage the research project together with the PI, Co-PI
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-edge optical microscopy systems for biomedical applications. This project involves the development of compact, label-free quantitative tomography systems and inverse scattering algorithms to push the
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grow. We welcome you to join our community of faculty, students and alumni who are shaping the future of AI, Data Science and Computing. Key Responsibilities: Designing and developing scalable algorithms
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will work closely with the Principal Investigator (PI), Co-PI, and the research team to develop deep learning-based computer vision algorithms and software for object detection, classification, and
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computational tools and algorithms, including emerging AI/ML based approaches, for genome annotation, comparative genomics, and multi-omics analyses, with particular attention to the challenges of complex
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on application logic rather than underlying algorithm development), and the capability of independently driving the full "Data-AI-Deployment" process; (c) the ability to evaluate the fit between AI applications
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market mechanisms. The successful candidate will work closely with colleagues across the project to develop analytical methods, algorithms, and visualisation tools that connect IoT monitoring with broader
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assurance/insurance frameworks, including algorithmic insurance contracts, parametric insurance structures, and data-driven risk quantification methods (e.g., conformal prediction and inverse conformal
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Center for Biologics Evaluation and Research (CBER) | Silver Spring, Maryland | United States | 1 day ago
learning algorithms, as well as the adaptation and optimization of existing tools. This research aligns with CBER’s efforts to enhance the development, operations, and management of FDA’s High-performance
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new deep learning algorithms for spatio-temporal medical image analysis with particular focus on learning from limited labelled data. Start date: Fall 2026 Duration: The appointment is for 3 years It is