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minimizing computational and energy costs. The proposed approaches will rely on machine learning methods applied to image analysis, with the objective of enabling early identification of at risk areas and
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specimens. The postdoc will contribute to the development of hybrid modeling and identification approaches that combine classical constitutive frameworks, numerical simulation, and machine learning. The work
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members have been working on statistics learning, granular computing and knowledge discovery, machine learning, deep learning, and specifically interpretable artificial intelligence. Many innovative
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members of the group help each other. Opportunity for growth as a software developer in areas such as CUDA programming, analysis of neural data, machine learning model applications, and real-time
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by Dr. Tim Pleskac (cognitive and decision modeling) and Dr. David Crandall (computer vision and AI). The postdoc will lead the development, integration, and testing of computational models of decision
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health. Visit https://www.umu.se/forskning/grupper/interactive-and-intelligent-systems/ for more information. Project description and work tasks We are seeking a candidate for a 3-year postdoc position
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structure and quantum chromodynamics, 2) Experience in the use of machine learning and high-performance numerical computations, 3) Readiness to teach courses in physics and computer science in English
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perform experiments using an array of state-of-the-art techniques from systems neuroscience, genetics, and physiology. More information about this lab can be found on his website https://knightlab.ucsf.edu
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. Selected postdocs will work with a primary mentor on projects at the intersection of educational data science, AI in education, and the learning sciences, with additional advisory support from faculty and
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separation on commercial recordings and extracting audio features (onsets, pitch, harmony, dynamics); curating datasets; and integrating machine learning approaches to complement rule-based methods. For more