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patients from public repositories (including dbGaP). Develop and apply machine learning algorithms to associate patterns in the data with cancer progression and therapeutic response in prostate cancer
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Massachusetts Institute of Technology | Cambridge, Massachusetts | United States | about 1 month ago
developing novel methods and algorithms for applications in the Engineering Design domain; demonstrating a commitment to deliver results; working on research proposals; and working in a team environment. Job
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will contribute to high-impact projects, including: 1. Developing and validating algorithms that extract data from the Epic EHR (e.g., large language models) via comparison with manually extracted data
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tasks (e.g. behaviour understanding and/or materials discrimination) across a range of imaging modalities. Specifically, they will investigate novel aspects of these tasks, develop software algorithms
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acts as a pathfinder for novel technologies and working methods across all space domains. The overarching goal of the research line is to develop and study advanced mathematical approaches, apply them
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or communications venues. Interest in mission-critical and critical infrastructure scenarios. What you will do Develop models, algorithms and optimization methods for resilient 6G transport networks, using machine
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of Defense (DoD) is offering a post-doctoral fellowship at the U.S. Army Medical Research and Development Command - Institute of Surgical Research (USAMRDC ISR) within the Expeditionary Medical Systems (EMS
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on the development of cutting-edge AI-ready/agentic infrastructure for data and model discovery, model metadata extraction, and scholarly intelligence. The project will explore conceptual frameworks and system
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PostDocs on the development of cutting-edge AI-ready/agentic infrastructure for data and model discovery, model metadata extraction, and scholarly intelligence. The project will explore conceptual frameworks
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National Lab, University of Tokyo etc.), the PhD candidate is expected to research on some of the following themes: New algorithms for parallel/distributed AI/ML Hardware-aware and resource-efficient