352 machine-learning-"https:"-"https:"-"https:"-"https:"-"https:"-"Iscte-IUL" Fellowship positions in United States
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Field
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the current and evolving labor market, particularly in key industries that rely on technological or human-machine learning, as well as those rooted in any other kind of disruptions that might impact the
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across the following research areas: Predictive machine learning Robust and stochastic optimization Learning-enabled control and reinforcement learning Power system operations, planning, and electricity
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, machine learning and AI-based computational tools to advance biomedical as well as oncology research. The fellow will work under the supervision of Dr. Veera Baladandayuthapani and there will be
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pathology whole slide image analysis are encouraged to apply. Additional preferred skills: Python programming Pathology whole slide image analysis Machine learning, especially generative adversarial networks
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to evaluate autonomous surgical robotics, mixed reality medical applications, and phantom-based alternatives. This project provides extensive learning and development opportunities across three critical areas
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languages (e.g., R, Python, Matlab) for data management and analysis, computational social science, and/or machine learning applications. Acquisition, processing, and analysis of remote sensing imagery
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, and AI/machine learning would be helpful for the role. Experience with participant recruitment and retention as well as clinical human subject studies is a plus. Special Instructions Application
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. Experience working with medical images. Very good computer programming skills and physics background is essential. Desired Qualifications* Nuclear medicine imaging/dosimetry experience. Experience in image
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Raman imaging technologies for safety and quality evaluation of agricultural products. Learn artificial intelligence/machine learning methods to evaluate hyperspectral image data to assess safety and
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, statistics, and field-lab approaches. Learning Objectives: The participant will receive training in plant molecular biology, genetics, and genomics. This research is expected to result in increased learning