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Field
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of causal reasoning tools, including causal inference, counterfactual analysis, causal discovery. Development of deep learning methods on computer vision. Job Requirements: Preferably PhD in Computer
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on advanced machine learning applications in computational pathology, medical imaging, and clinical text analysis. They will be expected to develop deep learning models for analyzing whole-slide histopathology
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+ benefits. AI and Deep Learning for Genomics, Transcriptomics, and Bioinformatics Job Summary The School of Biomedical Engineering at the University of British Columbia, Vancouver campus is seeking one
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health monitoring, preferably with a publication record in top-tier journals; and (c) be proficient in mainstream research frameworks for deep learning and computer vision. Applicants are invited
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at the John Curtin School of Medical Research (JCSMR), within the ANU College of Science and Medicine. This role will lead the development of advanced deep learning frameworks—including graph neural networks
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novel research methodologies in computer vision, deep learning architectures, and neuro-fuzzy systems to contribute to the development of robust AI frameworks for medical diagnosis and treatment support
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, mathematical criteria stability and robustness of neural networks, applications of topology and geometry to deep learning, the topology and geometry of data, or the dynamics of learning. The successful candidate
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Massachusetts Institute of Technology | Cambridge, Massachusetts | United States | about 8 hours ago
been extensively studied in game theory, reinforcement learning, and optimization, their full integration into modern AI systems (particularly in multi-agent deep learning and human-AI collaborative
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of performance, speed, and precision. Key Responsibilities: Design and implement genAI models for embodied AI systems. Develop and optimize deep learning algorithms to enable robotic arms to perform complex tasks
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, mathematical criteria stability and robustness of neural networks, applications of topology and geometry to deep learning, the topology and geometry of data, or the dynamics of learning. The successful candidate