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Field
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Fellow to develop and evaluate artificial intelligence methods for physical medical procedures. The fellow will design and implement machine learning models to analyze procedural data, support clinical
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We are seeking a Research Fellow - Data Science professional with strong expertise in machine learning, deep learning and high-frequency physiological signal analysis. This is a unique opportunity to
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Science, Materials Informatics, or a closely related discipline. 3–5 years of postdoctoral research experience with a strong publication record. Demonstrated expertise in machine learning for time-series or multimodal
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fluid mechanics, computational geometry, meshing, computational graphics, computational vision, or scientific machine learning in general. Successful candidates will join a community of researchers in
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technical depth in NLP methods and a clear interest in problems related to AI safety. Minimum Qualifications: PhD in Computer Science, Information Science, Computational Linguistics, Machine Learning, or a
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Oden Institute for Computational Engineering and Sciences | Austin, Texas | United States | 3 months ago
genetic data Multimodal machine learning for biological discovery Translational genomics and risk modeling The fellow will work in an environment that emphasizes methodological innovation, statistical rigor
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simulation platforms such as NVIDIA Isaac Sim, Isaac Lab, Gazebo, or MuJoCo. Familiarity with humanoid robots, robotic manipulators, or locomotion systems. Knowledge of machine learning, reinforcement learning
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insurance, supported by INESC TEC. 2. OBJECTIVES: • Explore machine learning approaches for discovering interpretable and clinically relevant visual representations.; • Validate the proposed methodologies
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transportation contexts, and machine learning classifiers Heuristics & Solvers: Develop and refine custom heuristics and metaheuristics (e.g., Tabu Search, Genetic Algorithms) to find high-quality solutions
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wide range of backgrounds and experiences. You should demonstrate: Essential Criteria Relevant academic training and a PhD (or equivalent experience) in a relevant subject such as health economics